Search

This paper presents a novel count time-series model, named integer-valued Split-BREAK process of the first order, abbr. INSB(1) model. This process is examined in terms of its basic stochastic properties, such as stationarity, mean, variance and correlation structure. In addition, the marginal distribution, over-dispersion and zero-inflation properties of the INSB(1) process are also examined. To estimate the unknown parameters of the INSB(1) process, an estimation procedure based on probability generating functions (PGFs) is proposed. For the obtained estimators, their asymptotic properties, as well as the appropriate simulation study, are examined. Finally, the INSB(1) process is applied in the dynamic analysis of some real-world series, namely, the numbers of serious traffic accidents in Serbia and forest fires in Greece. [ABSTRACT FROM AUTHOR]

In this paper we examine if the use of Hamilton's regression filter significantly modifies the cyclical components concerning unemployment in Greece compared with those using the Hodrick–Prescott double filter (HP). Hamilton suggested the use of a regression filter in order to overcome some of the drawbacks of the HP filter, which contains the presence of false cycles, the bias in the end of the sample, and the ad-hoc assumptions for the parameters' smoothing. Thus, our paper examines two widely used detrending methods for the extraction of cyclical components, including techniques of deterministic detrending as well as stochastic detrending. Using quarterly data for the unemployment of Greece in a macroeconomic model decomposition, we indicate that trend components and cycle components of Hamilton's filter regression led to significantly larger cycle volatilities than those from the HP filter. The dynamic forecasting in the sample, occurred both with autoregressive forecasting, that yields steady forecasts for a wide variety of non-stationary procedures, and with the HP filter, along with its constraints at the end of the time series. The results of the paper showed that the dynamic forecasting of the HP filter is better than that of Hamilton's in all assessment measures. [ABSTRACT FROM AUTHOR]

This paper reports the multifractal characteristics of lengthy PM10 time series from five stations in the Greater Athens Area (GAA), Greece. A novel methodology based on the multifractal detrended fluctuation analysis (MFDFA) is applied to raw and shuffled series in 74 segments in 11 date-periods, previously located, with very strong self-organised critical (SOC) and fractal properties. The MFDFA identified multifractality in all segments. Generalised and classical Hurst exponents are in the range 0.8–1.5 and 9–4.5 for the raw and shuffled series, while the multifractal f (a) − a is within 0.5–1.2 and 0.1–2, respectively. The f (a) − a data are fitted to polynomials to calculate the multifractal parameters W, F W H M and f m a x . While these are bimodal, a new parameter, F W H M / f m a x , is normally distributed, and due to this, it is employed to locate the important multifractal behaviour via the F W H M / f m a x outliers. Five date-periods are found. The date-period 8 January 2015 has extraordinary multifractality for raw and shuffled series for both the AGP and LYK stations. This date-period is one of the three reported in the most recent combination study. Finally, sliding window MFDFA evolution plots of all the series are given. The results provide very strong evidence of the multifractality of the PM10 time series. [ABSTRACT FROM AUTHOR]

The occurrence of flash floods in urban catchments within the Mediterranean climate zone has witnessed a substantial rise due to climate change, underscoring the urgent need for early-warning systems. This paper examines the implementation of an early flood monitoring and forecasting system (EMFS) to predict the critical overflow level of a small urban stream on Lesvos Island, Greece, which has a history of severe flash flood incidents requiring rapid response. The system is supported by a network of telemetric stations that measure meteorological and hydrometric parameters in real time, with a time step accuracy of 15 min. The collected data are fed into the physical Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS), which simulates the stream's discharge. Considering the HEC-HMS's estimated outflow and other hydro-meteorological parameters, the EMFS uses long short-term memory (LSTM) neural networks to enhance the accuracy of flood prediction. In particular, LSTMs are employed to analyze the real-time data from the telemetric stations and make multi-step predictions of the critical water level. Hydrological time series data are utilized to train and validate the LSTM models for short-term leading times of 15 min, 30 min, 45 min, and 1 h. By combining the predictions obtained by the HEC-HMS with those of the LSTMs, the EMFS can produce accurate flood forecasts. The results indicate that the proposed methodology yields trustworthy behavior in enhancing the overall resilience of the area against flash floods. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Implementation of environmental policies, is one of the European Commission's key priorities, as confirmed by its proposal for the 7th Environment Action Programme and the Roadmap to a resource-efficient Europe. Subsequently, the European Union revised its waste legislation to address these challenges more adequately. As a result, the EU's Waste Framework Directive and Landfill Directive set binding targets for recycling municipal waste and diverting biodegradable municipal waste from landfills. This paper deals with the monitoring of municipal waste management operations in Greece, in terms of technical content and ability to reach the targets of the initiatives. In addition, a more detailed report on waste indicators published by Eurostat will be based on an Exploratory Data Analysis and on a Univariate time-series prediction model. Finally, the overall aim of this study is to gain maximum insight into the data and understand its underlying structure through graphical representation, to achieve the most accurate forecast for the landfilling operation. [ABSTRACT FROM AUTHOR]

Purpose: A growing amount of micro-data analyses has highlighted the importance of information trails, such as generated by card transactions, for improving tax compliance. Yet, time series evidence indicating a positive effect of card payments on VAT revenue performance has been scarce. This paper revisits the question of the effect of card payments on VAT revenue by using annual and quarterly panel data for the 19 euro area member states, covering the period 2000–2016. Design/methodology/approach: A panel VECM is employed in order to address endogeneity issues and to account for common stochastic trends, which, is shown to be crucial in revealing the anticipated positive effect of card use on the performance of VAT. Findings: The analysis confirms that a higher share of card payments in private consumption increases VAT revenue and the efficiency of revenue collection. Higher gains are estimated for countries with above average self-employment. Originality/value: The contribution of the paper is twofold. First, to our knowledge it provides the first confirmation of the well-established literature on information trails using aggregate macroeconomic time series in a multi-country setting. Second, it has very timely policy implications, as low-hanging fruit are identified in euro-area economies with much to gain from strengthening the credibility of their fiscal performance, such as Greece. [ABSTRACT FROM AUTHOR]

Agricultural land extent and change information is needed to assess food security, the effectiveness of land use policy, and both environmental and societal impacts. This information is especially valuable in biodiversity hotspots such as the Mediterranean region, where agricultural land expansion can result in detrimental effects such as soil erosion and the loss of native species. There has also been a growing concern that changing agricultural extent in fire-prone regions of the Mediterranean may increase fire risk due to accumulation of fuel in abandoned areas. In this study, we assessed the extent and change of agricultural land in Southern Greece from 1986 to 2020 using a combined European Land Use/Cover Area frame Survey (LUCAS) and Landsat time series approach. The LUCAS data and Landsat spectral-temporal metrics were used to train a random forest classifier, which was used to classify arable land and permanent agriculture (e.g., olive orchards, vineyards) at annual time steps. A post-processing step was taken to reduce spurious landcover class transitions using transition likelihoods and annual class membership likelihoods. A validation dataset consisting of 2666 samples, identified via a stratified random sampling approach and high-resolution imagery and time series analysis, were used to evaluate stable and change strata accuracies. Overall accuracies were greater than 70% and strata-specific accuracies were highly variable between stable and change strata. The results show that southern Greece has experienced a recent gain in arable land (~12,000 ha from ~2009–2020) and a much larger gain in permanent agriculture (>115,000 ha from ~1993–2020). Arable land loss mainly occurred from 1987 to ~2002 when extent decreased by 15,000 ha, of which 66% was abandoned. The semi-automated approach described in this paper provides a promising approach for monitoring agricultural land change and enabling assessments of agriculture policy effectiveness and environmental impacts. [ABSTRACT FROM AUTHOR]

Vegetation index time-series analysis of multitemporal satellite data is widely used to study vegetation dynamics in the present climate change era. This paper proposes a systematic methodology to predict the Normalized Difference Vegetation Index (NDVI) using time-series data extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS). The key idea is to obtain accurate NDVI predictions by combining the merits of two effective computational intelligence techniques; namely, fuzzy clustering and long short-term memory (LSTM) neural networks under the framework of dynamic time warping (DTW) similarity measure. The study area is the Lesvos Island, located in the Aegean Sea, Greece, which is an insular environment in the Mediterranean coastal region. The algorithmic steps and the main contributions of the current work are described as follows. (1) A data reduction mechanism was applied to obtain a set of representative time series. (2) Since DTW is a similarity measure and not a distance, a multidimensional scaling approach was applied to transform the representative time series into points in a low-dimensional space, thus enabling the use of the Euclidean distance. (3) An efficient optimal fuzzy clustering scheme was implemented to obtain the optimal number of clusters that better described the underline distribution of the low-dimensional points. (4) The center of each cluster was mapped into time series, which were the mean of all representative time series that corresponded to the points belonging to that cluster. (5) Finally, the time series obtained in the last step were further processed in terms of LSTM neural networks. In particular, development and evaluation of the LSTM models was carried out considering a one-year period, i.e., 12 monthly time steps. The results indicate that the method identified unique time-series patterns of NDVI among different CORINE land-use/land-cover (LULC) types. The LSTM networks predicted the NDVI with root mean squared error (RMSE) ranging from 0.017 to 0.079. For the validation year of 2020, the difference between forecasted and actual NDVI was less than 0.1 in most of the study area. This study indicates that the synergy of the optimal fuzzy clustering based on DTW similarity of NDVI time-series data and the use of LSTM networks with clustered data can provide useful results for monitoring vegetation dynamics in fragmented Mediterranean ecosystems. [ABSTRACT FROM AUTHOR]

This paper investigates the market structure of the Greek manufacturing industry over the period 1984-2007 at the two-digit SIC level. Three models are used to investigate the competitive conditions of the manufacturing industry. The paper investigates factors affecting the markup of the Greek manufacturing industry by estimating two additional models, i.e., the markup cross-sectional model and the markup time-series model, for identifying both sectoral and intertemporal factors. The empirical results indicate that the whole Greek manufacturing industry, as well as each sector of the industry, operates in non-competitive conditions for each year during the period 1984-2007. The findings also support that labor intensity, the sector size, and the number of establishments influence the markup at the sectoral level, while labor intensity, and the growth and the number of establishments affect the markup over time. [ABSTRACT FROM AUTHOR]

In this paper, a novel combination of deep learning recurrent neural network and Lyapunov time is proposed to forecast the consumption of electricity load, in Greece, in normal/abrupt change value areas. Our method verifies the chaotic behavior of load time series through chaos time series analysis and with the application of deep learning recurrent neural networks produces predictions for 10 and 20 days ahead. Specifically, four different neural network models constructed (a) feed forward neural network, (b) gated recurrent unit (GRU) neural network, (c) long short-term memory (LSTM) recurrent and (d) bidirectional LSTM neural network to implement the prediction in a prediction horizon, produced through the extraction of maximum Lyapunov exponent. We constructed sequences of algorithms to feed the neural networks, creating three scenarios (a) 1-step, (b) 10-step and (c) 20-step sequences. For each neural network model, we used its predictions as inputs to predict steps forward, iteratively, to examine the accuracy of the proposed models, for horizons that are both inside and outside to that defined by Lyapunov time. The results show that the deep learning GRU neural network produces iterative predictions of high accuracy and stability, following the trend evolution of actual values, even outside the safe horizon for 1-step and 10-step cases. [ABSTRACT FROM AUTHOR]

This paper responds to a resurgence of interest in constructing long-term time proxies of human activity, especially but not limited to models of population change over the Pleistocene and/or Holocene. While very much agreeing with the need for this increased attention, we emphasize three important issues that can all be thought of as modifiable reporting unit problems: the impact of (i) archaeological periodization, (ii) uneven event durations and (iii) geographical nucleation-dispersal phenomena. Drawing inspiration from real-world examples from prehistoric Britain, Greece and Japan, we explore their consequences and possible mitigation via a reproducible set of tactical simulations. [ABSTRACT FROM AUTHOR]

A statistically efficient approach is adopted for modeling spatial time-series of large data sets. The estimation of the main diagnostic tool such as the likelihood function in Gaussian spatiotemporal models is a cumbersome task when using extended spatial time-series such as air pollution. Here, using the Innovation Algorithm, we manage to compute it for many spatiotemporal specifications. These specifications refer to the spatial periodic-trend, the spatial autoregressive moving average, the spatial autoregressive integrated and fractionally integrated moving average Gaussian models. Our method is applied to daily pollutants over a large metropolitan area like Athens. In the applied part of our paper, we first diagnose temporal and spatial structures of data using non-likelihood based criteria, such as the empirical autocorrelation and covariance functions. Second, we use likelihood and non-likelihood based criteria to select a spatiotemporal model among various specifications. Finally, using kriging we regionalize the resulting parameter estimates of the best-fitted model in space at any unmonitored location in the Athens region. The results show that a specific autoregressive integrated moving average spatiotemporal model can optimally perform in within and out of spatial sample estimation. Supplemental materials for this article are available from the journal website. [ABSTRACT FROM AUTHOR]

This paper tests two monetarist hypotheses on the Greek data: (1) the predictability of income velocity of money; and (2) the proportionality postulate between nominal income (or, prices) and money. The unit root tests with structural breaks show that the velocity of narrow money can be characterized as a stationary process. The Autoregressive Distributed Lag (ARDL) approach to cointegration indicates that the proportionality postulate between nominal income (or, prices) and money is supported by the data. This evidence suggests that shocks which affect the money supply are reflected in the nominal income (or, prices) in a similar way, thus velocity will not fluctuate widely and its movements will be predictable. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of different depreciation methods on the dynamic characteristics of the Greek regional net fixed capital series. Using annual data over the period from 1974 to 2006, Karpetis and Zikos (Int J Econ Res 11(2):333-354, 2014) constructed the series of nominal net fixed capital in the case of the thirteen administrative regions of Greece, applying four different depreciation methods of capital. In the context of the present analysis, these series were initially deflated, using the country's G.D.P. deflator (2005 = 100), and subsequently were exploited to investigate their dynamic characteristics through estimation of the best fitted ARIMA( p, d, q) models and determination of the roots of the characteristic polynomials. The statistical findings reveal, firstly, the impact of the employed depreciation methods of capital on the evolutionary pattern (monotonic or sinusoidal) of the series and secondly, the slow convergence of the Greek regional net fixed capital towards its long-run equilibrium value. [ABSTRACT FROM AUTHOR]

• The impact of COVID-19 on speed, speeding and harsh braking is evaluated. • SARIMA time-series modelling is used for quantifying the pandemic effect. • Naturalistic driving data captured from a novel smartphone applications are used. • Speeds and harsh brakings were found to have the highest increase. Introduction: COVID-19 has disrupted daily life and societal flow globally since December 2019; it introduced measures such as lockdown and suspension of all non-essential movements. As a result, driving activity was also significantly affected. Still, to-date, a quantitative assessment of the effect of COVID-19 on driving behavior during the lockdown is yet to be provided. This gap forms the motivation for this paper, which aims at comparing observed values concerning three indicators (average speed, speeding, and harsh braking), with forecasts based on their corresponding observations before the lockdown in Greece. Method: Time series of the three indicators were extracted using a specially developed smartphone application and transmitted to a back-end platform between 01/01/2020 and 09/05/2020, a time period containing normal operations, COVID-19 spreading, and the full lockdown period in Greece. Based on the collected data, XGBoost was employed to identify the most influential COVID-19 indicators, and Seasonal AutoRegressive Integrated Moving Average (SARIMA) models were developed for obtaining forecasts on driving behavior. Results: Results revealed the intensity of the impact of COVID-19 on driving, especially on average speed, speeding, and harsh braking per 100 km. More specifically, speeds were found to increase by 2.27 km/h on average compared to the forecasted evolution, while harsh braking/100 km increased to almost 1.51 on average. On the bright side, road crashes in Greece were reduced by 49% during the months of COVID-19 compared to the non-COVID-19 period. [ABSTRACT FROM AUTHOR]

This paper investigates the existence of chaos in concentration dynamics of particulate matter with an aerodynamic diameter less than or equal to 10 μ m ( PM 10 ) in the greater Athens area (GAA), Greece. It reports findings on three 16-year PM 10 time series recorded by three different air pollution monitoring stations located in GAA and examines if critical fractal epochs with long memory exist. Detrended Fluctuation Analysis (DFA) and Rescaled Range (R/S) Analysis were used via sliding windows of approximately 1-month duration. In all PM 10 time series, several segments were found with critical fractal behaviour and hidden long-memory patterns. All these segments exhibited Hurst exponents above 0.75 and DFA exponents above 1.75. Twelve PM 10 segments with fractality and long memory were commonly identified by both techniques. In one case, long memory was identified concurrently across all three air pollution monitoring stations and in another case, across two stations. The importance of the agreement between two different and independent chaos-analysis techniques is discussed in association with the proper selection of threshold values. This is the second time to address chaos in PM 10 data series in GAA, and the first time to combine two widely accepted techniques, DFA and R/S analysis. [ABSTRACT FROM AUTHOR]

In an epoch characterized by the swift pace of digitalization and urbanization, the essence of community well-being hinges on the efficacy of urban management. As cities burgeon and transform, the need for astute strategies to navigate the complexities of urban life becomes increasingly paramount. This study employs time series analysis to scrutinize citizen interactions with the coordinate-based problem mapping platform in the Municipality of Patras in Greece. The research explores the temporal dynamics of reported urban issues, with a specific focus on identifying recurring patterns through the lens of seasonality. The analysis, employing the seasonal decomposition technique, dissects time series data to expose trends in reported issues and areas of the city that might be obscured in raw big data. It accentuates a distinct seasonal pattern, with concentrations peaking during the summer months. The study extends its approach to forecasting, providing insights into the anticipated evolution of urban issues over time. Projections for the coming years show a consistent upward trend in both overall city issues and those reported in specific areas, with distinct seasonal variations. This comprehensive exploration of time series analysis and seasonality provides valuable insights for city stakeholders, enabling informed decision-making and predictions regarding future urban challenges. [ABSTRACT FROM AUTHOR]

Two microwave transponders have been operating in west Crete and Gavdos to calibrate international satellite radar altimeters at the Ku-band. One has been continuously operating for about 8 years at the CDN1 Cal/Val site in the mountains of Crete, and the other at the GVD1 Cal/Val site on Gavdos since 11 October 2021. This ground infrastructure is also supported at present by four sea-surface Cal/Val sites operating, some of them for over 20 years, while two additional such Cal/Val sites are under construction. This ground infrastructure is part of the European Space Agency Permanent Facility for Altimetry Calibration (PFAC), and as of 2015, it has been producing continuously a time series of range biases for Sentinel-3A, Sentinel-3B, Sentinel-6 MF, Jason-2, Jason-3, and CryoSat-2. This work presents a thorough examination of the transponder Cal/Val responses to understand and determine absolute biases for all satellite altimeters overflying this ground infrastructure. The latest calibration results for the Jason-3, Copernicus Sentinel-3A and -3B, Sentinel-6 MF, and CryoSat-2 radar altimeters are described based on four sea-surface and two transponder Cal/Val sites of the PFAC in west Crete, Greece. Absolute biases for Jason-3, Sentinel-6 MF, Sentinel-3A, Sentinel-3B, and CryoSat-2 are close to a few mm, determined using various techniques, infrastructure, and settings. [ABSTRACT FROM AUTHOR]

This paper is dealing with the assessment of climate change scenarios on selected meteorological variables at the Lake Karla watershed, central Greece and it is a contribution to the "HYDROMENTOR" research project. A statistical downscaling method has been employed to estimate monthly precipitation and temperature time series for future climate periods. The outputs of the Canadian Centre for Climate Modeling Analysis Global Circulation Model CGCM3 were applied for three socioeconomic scenarios, namely SRES B 1, SRES A 1B and SRES A2 for the assessment of climate change impact on precipitation and temperature. The methodology has been developed for the historical period 1980-2000 and applied for two future periods 2030-2050 and 2080-2100. Historical and future periods were compared for monthly and annual basis using various statistical criteria such as the mean and standard deviation. Results showed that climate change will have minor impacts on precipitation and even smaller impacts on temperature. [ABSTRACT FROM AUTHOR]

This paper reports one-month 3 kHz EM disturbances recorded at Kardamas, Ilia, Greece after a strong M L = 6.6 earthquake occurred on 2018/10/25 near Zakynthos and Ilia. During this period 17 earthquakes occurred with magnitudes M L = 4.5 and M L = 5.5 and depths between 3 km and 17 km, all near Zakynthos and Ilia. A two-stage, fully computational methodology was applied to the outcomes of five different time-evolving chaos analysis techniques (DFA, fractal dimension analysis through Higuchi, Katz and Sevcik methods and power-law analysis). Via literature-based thresholds, the out-of-threshold results of all chaos analysis methods were located and from these, the common time instances of 13 selected combinations per five, four, three and two methods. Numerous persistent segments were located with DFA exponents between 1.6 ≤ α ≤ 2.0 , fractal dimensions between 1.4 ≤ D ≤ 2.0 and power-law exponents between 2.2 ≤ β ≤ 3.0 . Out of the 17 earthquakes, six earthquakes were jointly matched by 13 selected combinations of five, four, three and two chaos analysis methods, four earthquakes by all combinations of four, three and two, while the remaining seven earthquakes were matched by at least one combination of three methods. All meta-analysis matches are within typical forecast periods. [ABSTRACT FROM AUTHOR]

This paper introduced a new ensemble learning approach, based on evolutionary fuzzy cognitive maps (FCMs), artificial neural networks (ANNs), and their hybrid structure (FCM-ANN), for time series prediction. The main aim of time series forecasting is to obtain reasonably accurate forecasts of future data from analyzing records of data. In the paper, we proposed an ensemble-based forecast combination methodology as an alternative approach to forecasting methods for time series prediction. The ensemble learning technique combines various learning algorithms, including SOGA (structure optimization genetic algorithm)-based FCMs, RCGA (real coded genetic algorithm)-based FCMs, efficient and adaptive ANNs architectures, and a hybrid structure of FCM-ANN, recently proposed for time series forecasting. All ensemble algorithms execute according to the one-step prediction regime. The particular forecast combination approach was specifically selected due to the advanced features of each ensemble component, where the findings of this work evinced the effectiveness of this approach, in terms of prediction accuracy, when compared against other well-known, independent forecasting approaches, such as ANNs or FCMs, and the long short-term memory (LSTM) algorithm as well. The suggested ensemble learning approach was applied to three distribution points that compose the natural gas grid of a Greek region. For the evaluation of the proposed approach, a real-time series dataset for natural gas prediction was used. We also provided a detailed discussion on the performance of the individual predictors, the ensemble predictors, and their combination through two well-known ensemble methods (the average and the error-based) that are characterized in the literature as particularly accurate and effective. The prediction results showed the efficacy of the proposed ensemble learning approach, and the comparative analysis demonstrated enough evidence that the approach could be used effectively to conduct forecasting based on multivariate time series. [ABSTRACT FROM AUTHOR]

Tide gauge recordings furnish the longest and almost the most continuous data source of sea level monitoring. Traditionally, they are collected using tide gauge instrumentation fixed at seaport locations to provide a time series of sea level estimates relative to a local geodetic benchmark. Sea level tidal observables are distinguished in the astronomical tide component originating from the attraction of the Earth–Moon–Sun gravitational system, and the storm surges ought to have meteorological effects due to wind and atmospheric air pressure variation. This study provides a comprehensive methodological approach and software to compute sea level considering astronomical tides enhanced by storm surge effects. The model is realized and assessed using a long-standing set of 21 consecutive years of tidal and meteorological measurements originating from Thermaikos Gulf, Greece. Analyses show model verification and conclusions about the tidal behavior of the test area, suggesting a satisfactory agreement (86% Willmott Skill factor, 9 cm standard deviation) between predicted and observed sea level estimates, accounting for amplitude and the time shift of skew surges. [ABSTRACT FROM AUTHOR]

A series of methodologies are described in this paper aiming to quantify the natural hazard due to the coastal changes at a deltaic fan. The coastline of Istiaia (North Evia, Greece) has been chosen for this study as several areas of accretion and erosion have been identified during the past few decades. We combined different types of datasets, extracted from high resolution panchromatic aerial photographs and traced the contemporary shoreline by high accuracy surveying with Real Time Kinematics (RTK) GPS equipment. The interpretation of all shorelines required geo-statistical analysis in a Geographical Information System. A large number of high resolution morphological sections were constructed normally to the coast, revealing erosional and depositional parts of the beach. Retreating and extension rates were calculated for each section reaching the values of 0.98 m/yr and 1.36 m/yr, respectively. The results proved to be very accurate, allowing us to expand the developed methodology by using more complete time-series of remote sensing datasets along with more frequent RTK-GPS surveying. [ABSTRACT FROM AUTHOR]

Open and analysis-ready data, as well as methodological and technical advancements have resulted in an unprecedented capability for observing the Earth's land surfaces. Over 10 years ago, Landsat time series analyses were inevitably limited to a few expensive images from carefully selected acquisition dates. Yet, such a static selection may have introduced uncertainties when spatial or inter-annual variability in seasonal vegetation growth were large. As seminal pre-open-data-era papers are still heavily cited, variations of their workflows are still widely used, too. Thus, here we quantitatively assessed the level of agreement between an approach using carefully selected images and a state-of-the-art analysis that uses all available images. We reproduced a representative case study from the year 2003 that for the first time used annual Landsat time series to assess long-term vegetation dynamics in a semi-arid Mediterranean ecosystem in Crete, Greece. We replicated this assessment using all available data paired with a time series method based on land surface phenology metrics. Results differed fundamentally because the volatile timing of statically selected images relative to the phenological cycle introduced systematic uncertainty. We further applied lessons learned to arrive at a more nuanced and information-enriched vegetation dynamics description by decomposing vegetation cover into woody and herbaceous components, followed by a syndrome-based classification of change and trend parameters. This allowed for a more reliable interpretation of vegetation changes and even permitted us to disentangle certain land-use change processes with opposite trajectories in the vegetation components that were not observable when solely analyzing total vegetation cover. The long-term budget of net cover change revealed that vegetation cover of both components has increased at large and that this process was mainly driven by gradual processes. We conclude that study designs based on static image selection strategies should be critically evaluated in the light of current data availability, analytical capabilities, and with regards to the ecosystem under investigation. We recommend using all available data and taking advantage of phenology-based approaches that remove the selection bias and hence reduce uncertainties in results. [ABSTRACT FROM AUTHOR]

This paper uses monthly data from the Athens Stock Exchange, over 1994-2007, to explore the relationship between risk aversion and market efficiency. The approach is based on econometric testing of the efficient markets model with varying discount factors, using an error-correction representation of the information variables. In this setting, we are able to evaluate the economic significance of the model by determining whether deviations, which produce statistical rejections, are transitory or not. The empirical evidence points to statistical rejections of efficiency in the Greek stock market which, nevertheless, appear to be transitory when risk averse behavior of investors is accounted for. [ABSTRACT FROM AUTHOR]

Ionospheric variability as a result of earthquake events is a confirmed phenomenon as published in various seismo-ionospheric coupling studies. Generally, ionospheric variations resulting from earthquake activity are much weaker than disturbances generated by different sources, e.g. geomagnetic storms. However, geomagnetic storm disturbances exhibit more global behaviour, whereas seismo-ionospheric variations occur only locally in an area that is specified by the magnitude of the earthquake. Cross-correlation coefficient analysis is a technique proposed some years ago, and ensures cancelation of geomagnetic storm variations of the ionospheric plasma, provided that the measurements are taken from stations with similar behaviour in these phenomena. In this paper we will use the aforementioned technique for analyzing data from ionospheric stations in Rome and Athens, and apply it to a series of earthquakes in Greece. Considering the local behaviour of the seismoionospheric variations, we expect that the Athens station, which happens to be inside the area affected by the earthquake, will accurately capture the disturbances. Due to its distance from the activity, we also do not expect the Rome station measurements to be affected by the seismic events in Greece. In addition, due to the fact that ionospheric plasma parameters exhibit non-stationary and nonlinear behaviour, we propose a novel signal processing technique known as the Hilbert-Huang transform in order to denoise the data before we calculate the cross-correlation coefficient of the two signals. Results from our analysis are in accordance with previously-conducted studies covering the same topic, clearly demonstrating that there are ionospheric precursors 1 to 7 days prior to strong seismic events as well as 1 to 2 days following such events. [ABSTRACT FROM AUTHOR]

Western Greece is one of the seismotectonically most active regions in Europe. The main tectonic structures are the West Hellenic Arc (WHA) and the Kephalonia Fault Zone. In order to monitor and understand the crustal movements in space and time, a continuous GPS network was installed. In this paper we present results of 6 yr (1995–2001) of measurements. To ensure a consistent reference frame, 54 mainly European IGS and EUREF sites were included in the processing. A selected subset was used to estimate an Euler pole for the rotation of Eurasia. In order to obtain coordinate time-series of high precision that are representative for crustal deformation, special emphasis was given to the elimination of non-tectonic effects. Four steps of improvement were pursued, including a reprocessing after exclusion of poor data, the removal of remaining outliers, the correction of unknown phase centre offsets after antenna changes and weighted common-mode filtering. With this procedure, non-tectonic irregularities were reduced significantly, and the precision was improved by an average of 40 per cent. The final time-series are used as a base for depicting trajectories of crustal motion, interpreting the temporal behaviour of the sites and for estimating velocities. For the first time, height changes in the WHA area were detected and quantified by GPS. Sites that are located near the epicentres of the 1997 Strofades and the 1999 Athens earthquakes are particularly considered. [ABSTRACT FROM AUTHOR]

The cointegration technique is now a common method of estimating any money demand function. Numerous studies that applied this technique to estimate the money demand function in Greece, interpreted their finding of cointegration as a sign of stable money demand. In this paper, after incorporating CUSUM and CUSUMSQ tests into cointegration analysis, we show that even though M1 and M2 monetary aggregates are cointegrated with income and interest rate, the M2 money demand function is unstable while M1 is stable. [ABSTRACT FROM AUTHOR]

Richardson's action-reaction model of an arms race has prompted a considerable body of research that has attempted to empirically estimate such models. In general these attempts have been unsuccessful. This paper provides an extensive reconsideration of the estimation issues and using some recent developments in time-series econometrics, provides a comprehensive analysis of data for Greece and Turkey. It finds evidence of some form of cointegration between the military expenditures in both countries, but not of Richardson arms race type. [ABSTRACT FROM AUTHOR]

This paper extends the literature on foreign banking by developing a model that attempts to explain the performance of Greek banks operating abroad using a balanced pooled time-series dataset. Five variables are drawn from the multinational banking literature and represent ownership-specific and location-specific factors. The profits of the subsidiaries operating abroad were found to be related to the profits of the parent bank, the trade between Greece and the host country, the difference in the GDP growth between the two countries, the years operating in the host market and the time trend. The size of the subsidiaries was found to be related to the size of the parent bank, the trade, the GDP growth, the years of operation and the time trend. Furthermore, the results indicate that models developed using variables drawn from the multinational banking literature provide a better description of the size of the subsidiaries of Greek banks operating abroad rather than their profits. [ABSTRACT FROM AUTHOR]

In this work, we analyze the high-frequency time series of the deposits collected from the systemic biggest Greek banks during the recent economic crisis in Greece. Our focus has been to reveal hidden fractal and periodic patterns using a hybrid approach, which combines correlation and frequency analysis of the original and difference series in a synergistic manner. We find that during the first period of the recorded series featured by the dramatic decrease of deposits, the short time behavior exhibits Brownian motion characteristics with fractal dimension close to 1.5, while a cyclical pattern of monthly repetition (21 days) is detected in the difference series to bound fractal behavior. The Brownian property is also observed in the second uprising segment of deposit series, but it lasts more (50 days) and no well-defined cyclical pattern is detected. Our work reveals that the economic crisis in Greece has gradually eroded the cyclical behavior of daily differences in bank deposits, while maintaining a short-term Brownian motion pattern, albeit with an increased duration. [ABSTRACT FROM AUTHOR]

The links between rapid channel degradation and related large wood (LW) recruitment and storage are still not precisely understood, especially in aquatic systems with high human and natural pressures such as intermittent rivers in the peri‐Mediterranean region. In this study, by using multidisciplinary research (analysis of satellite images time series, detailed field inventory, and simulated hydrological data), we focused on rapid upstream propagation of channel degradation (i.e., incision and consequent channel widening) in an intermittent Mediterranean river (Evrotas River, southern Greece) as a response to short‐term intensive gravel extraction from the active channel and removal of riparian vegetation in the 2013–2016 period. Subsequently, our objective was to link this channel transformation with the spatiotemporal changes of LW storage in the active channel. We found that short‐term and spatially limited human impact, coupled with high flows, allowed rapid upstream progression (~2 km) of erosion processes (approximately twofold widening of the active channel) during the relatively short 2‐year period (2017–2019). This morphological response accelerated LW recruitment through bank erosion and its subsequent storage in the active channel. Incision and channel widening processes were interrupted in the 2019–2021 period due to the prevalence of relatively low, geomorphologically non‐effective flows. However, the storage of LW in the channel continued to increase as a result of the higher trapping efficiency of the widened channel, a delay between tree death and its final uprooting, or by LW recruitment processes not directly related to flood events. We suggest that freshly recruited LW has the potential to accelerate the establishment of a new channel equilibrium and increase channel heterogeneity through its interaction with flows and sediments. [ABSTRACT FROM AUTHOR]

High mountain zones in the Mediterranean area are considered more vulnerable in comparison to lower altitudes zones. Lefka Ori massif, a global biodiversity hotspot on the island of Crete is part of the Global Observation Research Initiative in Alpine Environments (GLORIA) monitoring network. The paper examines species and vegetation changes with respect to climate and altitude over a seven-year period (2001–2008) at a range of spatial scales (10 m Summit Area Section-SAS, 5 m SAS, 1 m2) using the GLORIA protocol in a re-survey of four mountain summits (1664 m–2339 m). The absolute species loss between 2001–2008 was 4, among which were 2 endemics. At the scale of individual summits, the highest changes were recorded at the lower summits with absolute species loss 4 in both cases. Paired t-tests for the total species richness at 1 m2 between 2001–2008, showed no significant differences. No significant differences were found at the individual summit level neither at the 5 m SAS or the 10 m SAS. Time series analysis reveals that soil mean annual temperature is increasing at all summits. Linear regressions with the climatic variables show a positive effect on species richness at the 5 m and 10 m SAS as well as species changes at the 5 m SAS. In particular, June mean temperature has the highest predictive power for species changes at the 5 m SAS. Recorded changes in species richness point more towards fluctuations within a plant community's normal range, although there seem to be more significant diversity changes in higher summits related to aspects. Our work provides additional evidence to assess the effects of climate change on plant diversity in Mediterranean mountains and particularly those of islands which remain understudied. [ABSTRACT FROM AUTHOR]

This study aims to present and analyze the time series of the snow parameters focusing on representative geographical areas of the Eastern Mediterranean (i.e., Greece and Italy) and to examine their seasonal variability, in terms of region and geography. The satellite retrievals were firstly validated against in-situ retrievals for 67 common days, with a mean bias equal to −0.018 cm, with a near-Gaussian distribution, showing the good performance of the satellite snow detection. The satellite-based analysis resulted in increasing trends of snow water equivalent, attributed to the enhanced values between 2000 and 2009; however, decreasing trends are found starting from 2010 until now of −1.79 × 10−17 and −2.31 × 10−18 over the two representative areas of Greece (e.g., Thessaloniki and Kozani). A similar pattern is found for the snow water equivalent in the Italian study area, with a decreasing trend of −4.45 × 10−18. The presented results contribute to a better understanding of the spatial snow distribution and the snow coverage seasonality that could be crucial for the long-term groundwater management, by combining snow data trends from in-situ data and satellite statistics. [ABSTRACT FROM AUTHOR]

The impact of COVID-19 and the pressure it exerts on health systems worldwide motivated this study, which focuses on the case of Greece. We aim to assist decision makers as well as health professionals, by estimating the short to medium term needs in Intensive Care Unit (ICU) beds. We analyse time series of confirmed cases, hospitalised patients, ICU bed occupancy, recovered patients and deaths. We employ state-of-the-art forecasting algorithms, such as ARTXP, ARIMA, SARIMAX, and Multivariate Regression models. We combine these into three forecasting models culminating to a tri-model approach in time series analysis and compare them. The results of this study show that the combination of ARIMA with SARIMAX is more accurate for the majority of the investigated regions in short term 1-week ahead predictions, while Multivariate Regression outperforms the other two models for 2-weeks ahead predictions. Finally, for the medium term 3-weeks ahead predictions the Multivariate Regression and ARIMA with SARIMAX show the best results. We report on Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), R-squared ( R 2 ), and Mean Absolute Error (MAE) values, for one-week, two-week and three-week ahead predictions for ICU bed requirements. Such timely insights offer new capabilities for efficient management of healthcare resources. [ABSTRACT FROM AUTHOR]

So far, empirical research on an ex-post benchmark of the euro adoption has relied on the synthetic control method by Abadie & Gardeazabal (Am Econ Rev 93:112-132, 2003) and Abadie et al. (J Am Stat Assoc 105:493-505, 2010, Am J Polit Sci, 59:495-510, 2015). However, the evidence obtained with this method is not overly consistent, leading to the conclusion that the method is not too robust to different settings of the adjustment screws. Using a new method developed by Harvey & Thiele (J Appl Econ 36:71–85, 2021) based on structural time series models, I find that France and Italy are clear losers from the euro while there are no real winners until 2019. Spain, Netherlands, Greece gain in the period before the financial crisis, but afterwards they lose. In fact, only the German economy is robust to the two crises but it loses until 2008. Relating the theories of optimum currency areas to the estimated gaps of the euro adopters from their synthetic controls, I find that openness, real convergence, and net migration are the main drivers of gains from a euro adoption, while the main drivers of losses are low levels of competitiveness, fiscal instability and labour market rigidity. Examining the crisis channels of the financial and euro crisis shows that fiscal instability, labour market rigidity and also business cycle synchronization cause large losses during the crises. Net migration helps to dampen these shocks. While openness is beneficial during the pre-crises period, it cannot help dampen the shock of the crises. [ABSTRACT FROM AUTHOR]

Background: During the recent pandemic, Healthcare Professionals (HCPs) presented a significant prevalence of psychological health problems and sleep disturbances. The aim of this study was to assess the impact of COVID-19 on HCPs' sleep and mental stress with a separate analysis for primary care HCPs. Methods: A cross-sectional observational study with an online anonymized, self-reported questionnaire was conducted in May 2020 (1st wave) and repeated in December 2020 (2nd wave). Patient health questionnaire-4 (PHQ-4), dimensions of anger reactions-5 (DAR-5) scale, 3-item UCLA loneliness scale (LS) and sleep condition indicator (SCI) were used. Results: Overall, 574 participants were included from the 1st wave, 514 from the 2nd and 469 were followed during both. Anxiety and depression were significantly higher during the 2nd wave vs. the 1st (32.8% vs. 12.7%, p < 0.001 and 37.7% vs. 15.8%, p < 0.001). During the 2nd wave, HCPs scored significantly higher in DAR-5 (9.23 ± 3.82 vs. 7.3 ± 3.3, p < 0.001) and LS (5.88 ± 1.90 vs. 4.9 ± 1.9, p < 0.001) with worse sleep quality SCI (23.7 ± 6.6 vs. 25.4 ± 3.2, p < 0.001). This was more evident in primary care HCPs. Significant correlations were found between SCI and PHQ4, DAR5 and LS. Conclusion: There is a need to support HCPs' mental health and sleep, especially in those working in primary care. [ABSTRACT FROM AUTHOR]

Spaceborne Synthetic Aperture Radar (SAR) are well-established systems for flood mapping, thanks to their high sensitivity towards water surfaces and their independence from daylight and cloud cover. Particularly able is the 2014-launched Copernicus Sentinel-1 C-band SAR mission, with its systematic monitoring schedule featuring global land coverage in a short revisit time and a 20 m ground resolution. Yet, variable environment conditions, low-contrasting land cover, and complex terrain pose major challenges to fully automated flood monitoring. To overcome these issues, and aiming for a robust classification, we formulate a datacube-based flood mapping algorithm that exploits the Sentinel-1 orbit repetition and a priori generated probability parameters for flood and non-flood conditions. A globally applicable flood signature is obtained from manually collected wind- and frost-free images. Through harmonic analysis of each pixel's full time series, we derive a local seasonal non-flood signal comprising the expected backscatter values for each day-of-year. From those predefined probability distributions, we classify incoming Sentinel-1 images by simple Bayes inference, which is computationally slim and hence suitable for near-real-time operations, and also yields uncertainty values. The datacube-based masking of no-sensitivity resulting from impeding land cover and ill-posed SAR configuration enhances the classification robustness. We employed the algorithm on a 6-year Sentinel-1 datacube over Greece, where a major flood hit the region of Thessaly in 2018. In-depth analysis of model parameters and sensitivity, and the evaluation against microwave and optical reference flood maps, suggest excellent flood mapping skill, and very satisfying classification metrics with about 96% overall accuracy and only few false positives. The presented algorithm is part of the ensemble flood mapping product of the Global Flood Monitoring (GFM) component of the Copernicus Emergency Management Service (CEMS). [ABSTRACT FROM AUTHOR]

Inland water resources are facing increasing quantitative and qualitative pressures, deriving from anthropogenic causes and the ongoing climate change. The monitoring of reservoirs is essential for sustainable management and preparation against water scarcity and extreme events, such as droughts. This research, relying on the Sentinel-2 and 3 missions, attempts to demonstrate the efficiency of combining remotely sensed water level and water area estimations, in order to estimate the water storage variation of Yliki reservoir. The case study is conducted in one of the few sufficiently monitored reservoirs in Greece, enabling a direct comparison of the proposed methodology results with in situ observations. Moreover, this research work proposes a weekly time interval for pairing level and area estimations, instead of shorter time intervals. The results strongly demonstrate the efficiency of remote sensing in the production of empirical level–area–storage (L–A–S) curves. Correlation to in situ monitored storage- and satellite-derived water level, area stand for 98.81% and 99.27% respectively. Water storage variation is estimated and compared to the observed time series, resulting in an RMSE of 1.28% of the reservoir capacity and a correlation of 96.14%. The empirical L–S relationship underestimates storage, while the A–S relationship overestimates storage when compared to the existing L–A–S curve. [ABSTRACT FROM AUTHOR]

Two different approaches of two different time-series models were used to predict the critical frequency (foF2) of the ionospheric F2 layer over Athens (38.0° N, 23.5° E), Greece. Experimental foF2 data were obtained for the Athens station between 2004 and 2018. For the foF2 prediction, the R language forecasthybrid model library time-series convenient functions were used. Root mean square error (RMSE), mean absolute percent error (MAPE) and mean absolute error (MAE) performance metrics were used to examine the performances of the models. According to these tests, the predictions made with the cross-validation error approach are somewhat better than the equal-weighted-prediction approach. Besides, the predictions of foF2 were compared with those of the IRI-2016 model. According to the RMSE, MAE and MAPE analysis, the values of IRI-2016 model were 1.17 MHz, 1.00 MHz and 29.67%, whereas those of EWP and cross-validation errormodel were 0.40, 0.28 and 5.60 and 0.40, 0.27 and 5.56, respectively. Therefore, it can be said that the predictions of these time-series approaches used for the first time for the prediction of ionospheric foF2 are better than those of the IRI-2016. As a result, time-series algorithms, which are now living in the golden age, offer new opportunities for the prediction of ionospheric parameters as in other fields. [ABSTRACT FROM AUTHOR]

Little is known about the reproductive behavior of the population in insular Greece because of the lack of reliable and unintermittent time series of vital statistics. By applying methods used in historical demography and by indirectly estimating demographic indexes from census returns we try to overcome data scarcity. Substantial differences were found between the islands and mainland Greece. Differences were also found between different groups of islands, with the North Aegean being more conservative as far as the procreative ethics are concerned. Out of wedlock childbearing is less common in North Aegean islands than in Dodecanese or in the Cyclades. [ABSTRACT FROM AUTHOR]

Abstract: As broadband access networks are relatively new in Greece, it is really difficult and involves great risk to determine the potential market for broadband services. The intent of this paper is to add to the discussion of delivering fixed broadband lines to customers in Greece taking into account regulation issues, the strategic movements of the market key players and the commonly admitted inequality of broadband access availability between urban and rural areas. In addition, using time-series analysis and examining the correlations between the number of fixed broadband lines and specific demographic factors for the past three years (mid 2006–mid 2009), an attempt to estimate empirically the forthcoming demand for broadband lines is conducted. Population’s income and effective competition are recognized as the strongest determinants of broadband development. In addition, it yields that demand for broadband will continue to have a positive trend for the years to come as people are becoming more and more familiar with new technologies, with youth being the pioneer to the use of innovative services that require broadband access. [ABSTRACT FROM AUTHOR]

Abstract: During the past few years, researchers have found evidence indicating that various time series representing daily cycles of traffic, such as volumes, speeds and occupancies, may be nonlinear. In this paper it is shown that such nonlinearities can be adequately described by smooth-transition regression (STR) models which may characterize distinct regimes for free flow, congestion, and asymmetric behavior in the transition phases from free flow to congestion and vice versa. STR models are advantageous compared to the – frequently adopted in traffic modeling – Artificial Neural Networks models because their parameters are interpretable. An exposition of smooth transition models is presented with a focus on logistic multi-regime models that are deemed to be most appropriate for modeling traffic variables. The methodology is illustrated by an application to data on speeds volumes and occupancies, obtained from two loop detectors located in a major arterial of Athens, Greece. Tests on nonlinearity provided ample evidence of regime-dependent dynamics for all traffic time series examined. The following research questions are examined using STR models: How many regimes, described by linear dynamics, characterize the cycle of traffic for a typical weekday? Which time interval corresponds to each regime and how fast is the transition from one regime to another? Are the estimated dynamics of daily cycles stable across weekdays? [Copyright &y& Elsevier]

The paper presents an algorithmic approach for analysis of statistical data on quantity of fish catches stored in time series datasets. The developed algorithm applies trend modeling and categorizing rules for processing total data on fish species catches as well as data on fish species catches by areas. This algorithm finds out the fish species that might be at risk and groups them accordingly into the following four categories: a) economical, b) biological, c) biodiversity and d) biological and biodiversity. The analysis of these categories supports planning for future activities referring to the sustainability of the fishery ecosystem in Greece. The presented algorithm is applied on the sea fishery time series data from Greece, but it can also be applied on the same data from other countries or on the same type of integrated data from many countries belonging to big fishing areas (e.g. the Mediterranean Sea) towards data mining of fish species at risk. [ABSTRACT FROM AUTHOR]

Summary: River flow forecasting consists one of the most important applications in modern hydrology, especially for the effective hydropower reservoir management. In this paper, an innovative non-linear time-series fitting and forecasting model is proposed, consisting of the following sub-modules: (a) the division of the time-series into generations, by identifying the structural change points, (b) the generation decomposition into linear trend, harmonic component and autoregressive component, consisting of several gene ARMA models, (c) the use of fuzzy methods to determine the relative weight of each gene model, and (d) the time-series expansion for the optimal forecasting. The method was applied to the mean monthly Nestos River discharge data for the 1966–2006 period, recorded at the Greek–Bulgarian border, serving as inflow to the Thissavros Hydropower Reservoir. The selected series was divided into five distinctive generations representing periods of gradual surface runoff reduction. It occurred that mean monthly discharge during the fifth generation was almost halved, compared to the corresponding value of the first generation. Harmonic decomposition produced periods in agreement with the large-scale atmospheric fluctuations, while ARIMA modeling performed on the residuals from a pre-defined gene model-base, produced satisfactory fitting to the measured flow series. Model expansion for the last 2 years (2005–2006) of the time-series illustrated reasonable good approximations. Model forecasts during 2005 followed closely the recorded variability, with MAPE and RMSE statistics indicating increased model accuracy. Overall, it is evident that the model slightly underpredicts Nestos River discharge, while slight overestimation occurs under high river flow conditions (winter 2006). Although current research proved model’s distinct capability and advantages in river flow fitting and medium-scale forecast, future improvement could facilitate the use of more elaborative models (ARCH, fuzzy, ANN), in the developed gene model-base. [Copyright &y& Elsevier]

Tourists from USA, which is Greece's third major market after the European Union and Japan, represent 6% of international tourist arrivals in Greece in 2002. The average annual growth rate of tourist arrivals from USA was 20% for the period 1960-2000. Despite the political, economic and military crises, which dominated in the examined period, tourist arrivals to Greece from USA have continued to grow slowly. Therefore, it is essential to consider the economic factors influencing this tourism demand. The purpose of the paper is to estimate the elasticities among origin country's income, consumer price indices of both countries and transportation cost of inbound tourism from USA to Greece, exchange rate, and prices of goods and services of a competitive country using seasonally unadjusted quarterly data. Given Turkey's proximity to Greece, it is also useful to determine whether Greece and Turkey are substitute or complementary destinations using tourism demand models. In addition, vector error correction models (VECM) are examined in order to illustrate the seasonal effect of tourism demand in USA for Greece. [ABSTRACT FROM AUTHOR]

Copyright of Transformations in Business & Economics is the property of Vilnius University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)