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The time series analysis and prediction techniques are highly valued in many application fields, such as economy, medicine and biology, environmental sciences or meteorology, among others. In the last years, there is a growing interest in the sustainable and optimal management of a resource as scarce as essential: the water. Forecasting techniques for water management can be used for different time horizons from the planning of constructions that can respond to long-term needs, to the detection of anomalies in the operation of facilities or the optimization of the operation in the short and medium term. In this paper, a deep neural network is specifically designed to predict water consumption in the short-term. Results are reported using the time series of water consumption for a year and a half measured with 10-min frequency in the city of Murcia, the seventh largest city in Spain by number of inhabitants. The results are compared with K Nearest Neighbors, Random Forest, Extreme Gradient Boosting, Seasonal Autoregressive Integrated Moving Average and two persistence models as naive methods, showing the proposed deep learning model the most accurate results. [ABSTRACT FROM AUTHOR]

Potable water utilities are currently making great efforts to reduce leakage rates and assure long-term supply to the population due to the challenges of climate change, growing population and water shortage scenarios that have been on them over the last years. One of the most employed methods to reduce leakage includes the installation of pressurereducing valves along the water distribution network and the utilization of pressure management schemes. Pressure management includes different types of control models, which are applied according to the requirements of each site. The most advanced and sophisticated scheme is critical point control, which relies on a flow signal from a measuring device or online communication between the critical point and the valve. This paper proposes the utilization of a seasonal autoregressive integrated moving average, or the SARIMA model, to correlate pressure at the outlet of the valve and pressure on the critical point of the area supplied, aiming to set a fixed pressure in the critical point. The SARIMA model is developed according to historical data logged in the field and then validated. Later, the SARIMA model was tested on a real location in the village of Noja, Spain. The analysis of the field test results prove that the proposed model is feasible to be used since there is no significance difference between the target values set in the critical point and the real values measured in the field. The research proves that the SARIMA model can be used as an alternative for critical point control in water distribution networks when no flow signal is available or when communication between the critical point and the pressure reducing valve is not an option. [ABSTRACT FROM AUTHOR]

From a public health perspective, tobacco use is addictive by nature and triggers several cancers, cardiovascular and respiratory diseases, reproductive disorders, and many other adverse health effects leading to many deaths. In this context, the need to eradicate tobacco-related health problems and the increasingly complex environments of tobacco research require sophisticated analytical methods to handle large amounts of data and perform highly specialized tasks. In this study, time series models are used: autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) to forecast the impact of COVID-19 on sales of cigarette in Spanish provinces. To find the optimal solution, initial combinations of model parameters automatically selected the ARIMA model, followed by finding the optimized model parameters based on the best fit between the predictions and the test data. The analytical tools Autocorrelation Function (ACF), Partial Autocorrelation Function (PACF), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) were used to assess the reliability of the models. The evaluation metrics that are used as criteria to select the best model are: mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), mean percentage error (MPE), mean error (ME) and mean absolute standardized error (MASE). The results show that the national average impact is slight. However, in border provinces with France or with a high influx of tourists, a strong impact of COVID-19 on tobacco sales has been observed. In addition, the least impact has been observed in border provinces with Gibraltar. Policymakers need to make the right decisions about the tobacco price differentials that are observed between neighboring European countries when there is constant and abundant cross-border human transit. To keep smoking under control, all countries must make harmonized decisions. [ABSTRACT FROM AUTHOR]

Background: In line with WHO guidelines for the implementation of public health prevention plans targeted at the impacts of high temperatures, a heat wave definition temperature (Tthreshold) was calculated for 182 so-called "isoclimatic zones" (IZ) in Spain. As the dependent variable for determining this Tthreshold, we analysed daily all-cause mortality data (ICD-10: A00-R99) for each IZ across the period 2009–2018. The independent variable used was the mean value of the maximum daily temperature of the summer months recorded at meteorological observatories in each IZ. We used Box–Jenkins models to ascertain mortality anomalies, and scatterplots to link these anomalies to the temperatures at which they occurred, thereby determining the Tthreshold for each IZ. We then calculated how many heat waves had occurred in each IZ, as well as their intensity, and analysed their time trend over this period. Results: The results showed that in 52.5% of the IZ, the percentile of the maximum temperatures series of the summer months to which Tthreshold corresponded was below the 95th percentile of the meteorological heat wave definition in Spain: indeed, it only coincided in 30.7% of cases. The geographical distribution of these percentiles displayed great heterogeneity as a consequence of the local factors that influence the temperature–mortality relationship. The trend in the number of heat waves analysed indicated an overall increase in Spain at a rate of 3.9 heat waves per decade, and a similar rise in mean annual intensity of 9.5 °C/decade. These time-trend values were higher than those yielded by analysing the trend in meteorological heat waves based on the 95th percentile. Conclusions: The results obtained in this study indicate the need to use a heat wave definition based on epidemiological temperature–mortality studies, rather than on values based on meteorological percentiles. This could be minimising estimated health impacts in analyses of future impacts attributable to heat. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, next-day hourly forecasts are calculated for the energy price in the electricity production market of Spain. The methodology used to achieve these forecasts is based on artificial neural networks, which have been used successfully in recent years in many forecasting applications. The days to be forecast include working days as well as weekends and holidays, due to the fact that energy price has different behaviours depending on the kind of day to be forecast. Besides, energy price time series are usually composed of too many data, which could be a problem if we are looking for a short period of time to reach an adequate forecast. In this paper, a training method for artificial neural nets is proposed, which is based on making a previous selection for the multilayer perceptron (MLP) training samples, using an ART-type neural network. The MLP is then trained and finally used to calculate forecasts. These forecasts are compared to those obtained from the well-known Box–Jenkins ARIMA forecasting method. Results show that neural nets perform better than ARIMA models, especially for weekends and holidays. Both methodologies calculate more accurate forecasts—in terms of mean absolute percentage error—for working days that for weekends and holidays. Agents involved in the electricity production market, who may need fast forecasts for the price of electricity, would benefit from the results of this study. [Copyright &y& Elsevier]

Abstract: This paper proposes a novel price forecasting method based on wavelet transform combined with ARIMA and GARCH models. By wavelet transform, the historical price series is decomposed and reconstructed into one approximation series and some detail series. Then each subseries can be separately predicted by a suitable time series model. The final forecast is obtained by composing the forecasted results of each subseries. This proposed method is examined on Spanish and PJM electricity markets and compared with some other forecasting methods. [ABSTRACT FROM AUTHOR]

Background: The automated collection of non-specific data from livestock, combined with techniques for data mining and time series analyses, facilitates the development of animal health syndromic surveillance (AHSyS). An example of AHSyS approach relates to the monitoring of bovine fallen stock. In order to enhance part of the machinery of a complete syndromic surveillance system, the present work developed a novel approach for modelling in near real time multiple mortality patterns at different hierarchical administrative levels. To illustrate its functionality, this system was applied to mortality data in dairy cattle collected across two Spanish regions with distinct demographical, husbandry, and climate conditions. Results: The process analyzed the patterns of weekly counts of fallen dairy cattle at different hierarchical administrative levels across two regions between Jan-2006 and Dec-2013 and predicted their respective expected counts between Jan-2014 and Jun- 2015. By comparing predicted to observed data, those counts of fallen dairy cattle that exceeded the upper limits of a conventional 95% predicted interval were identified as mortality peaks. This work proposes a dynamic system that combines hierarchical time series and autoregressive integrated moving average models (ARIMA). These ARIMA models also include trend and seasonality for describing profiles of weekly mortality and detecting aberrations at the region, province, and county levels (spatial aggregations). Software that fitted the model parameters was built using the R statistical packages. Conclusions: The work builds a novel tool to monitor fallen stock data for different geographical aggregations and can serve as a means of generating early warning signals of a health problem. This approach can be adapted to other types of animal health data that share similar hierarchical structures. [ABSTRACT FROM AUTHOR]

The relationship between heat waves and mortality has been widely described, but there are few studies using long daily data on specific-cause mortality. This study is undertaken in central Spain and analysing natural causes, circulatory and respiratory causes of mortality from 1975 to 2008. Time-series analysis was performed using ARIMA models, including data on specific-cause mortality and maximum and mean daily temperature and mean daily air pressure. The length of heat waves and their chronological number were analysed. Data were stratified in three decadal stages: 1975-1985, 1986-1996 and 1997-2008. Heat-related mortality was triggered by a threshold temperature of 37 °C. For each degree that the daily maximum temperature exceeded 37 °C, the percentage increase in mortality due to circulatory causes was 19.3 % (17.3-21.3) in 1975-1985, 30.3 % (28.3-32.3) in 1986-1996 and 7.3 % (6.2-8.4) in 1997-2008. The increase in respiratory cause ranged from 12.4 % (7.8-17.0) in the first period, to 16.3 % (14.1-18.4) in the second and 13.7 % (11.5-15.9) in the last. Each day of heat-wave duration explained 5.3 % (2.6-8.0) increase in respiratory mortality in the first period and 2.3 % (1.6-3.0) in the last. Decadal scale differences exist for specific-causes mortality induced by extreme heat. The impact on heat-related mortality by natural and circulatory causes increases between the first and the second period and falls significantly in the last. For respiratory causes, the increase is no reduced in the last period. These results are of particular importance for the estimation of future impacts of climate change on health. [ABSTRACT FROM AUTHOR]

Studies on temperature-mortality time trends especially address heat, so that any contribution on the subject of cold is necessarily of interest. This study describes the modification of the lagged effects of cold on mortality in Castile-La Mancha from 1975 to 2003, with the novelty of also approaching this aspect in terms of mortality trigger thresholds. Cross-correlation functions (CCFs) were thus established with 15 lags, after application of ARIMA models to the mortality data and minimum daily temperatures (from November to March), and the results for the periods 1975-1984, 1985-1994 and 1995-2003 were then compared. In addition, daily mortality residuals for the periods 1975-1989 and 1990-2003 were related to minimum temperatures grouped in 2°C intervals, with a cold threshold temperature being obtained in cases where such residuals increased significantly ( p < 0.05) with respect to the mean for the study period. A cold-related mortality trigger threshold of −3°C was obtained for Ciudad Real for the period 1990-2003. The significant number of lags ( p < 0.05) in the CCFs declined every 10 years in Toledo (5-2-0), Cuenca (4-2-0), Albacete (4-3-0) and Ciudad Real (3-2-1). This meant that, while the trend in cold-related mortality trigger thresholds in the region could not be ascertained, it was possible to establish a reduction in the lagged effects of cold on mortality, attributable to the improvement in socio-economic conditions over the study period. Evidence was shown of the effects of cold on mortality, a finding that renders the adoption of preventive measures advisable in any case where intense cold is forecast. [ABSTRACT FROM AUTHOR]

lternaria solani Soraeur produces early blight in Solanum tuberosum L., leading to significant agricultural losses. The current study was carried out on the extensive potato crop situated in north-western of Spain during 2007, 2008 and 2009. In this area potato crops are the most important source of income. In this work we used a Hirst-type volumetric spore-trap for the aerobiological monitoring of Alternaria spores. The highest spore concentrations were recorded during the 2009 cycle (10,555 spores), and the lowest concentrations were recorded during the 2008 cycle (5,471 spores). Over the 3 years of study, the highest concentrations were registered during the last stage of the crop. The aim of the study was to observe the influence of meteorological factors on the concentration of Alternaria spores, which can lead to serious infection and early blight. Prediction of the stages during which a crop is particularly vulnerable to infection allows for adjustment of the application of fungicide and is of environmental and agricultural importance. For this reason, we tested three models (P-Days, DD and IWP) to predict the first treatment and decrease the negative effect that these spores have on potato crops. The parameter that showed the most significant correlation with spore concentrations was minimum temperature. We used ARIMA (autoregressive integrated model of running mean) time-series models to determine the forecast. We considered weather data as predictor variables and the concentration of spores on the previous day as the fixed variable. [ABSTRACT FROM AUTHOR]

Time series of mean monthly temperature and total monthly precipitation are two of the climatic variables most easily obtained from weather station records. There are many studies analyzing historical series of these variables, particularly in the Spanish territory. In this study, the series of these two variables in 47 stations of the provincial capitals of mainland Spain were analyzed. The series cover time periods from the 1940s to 2013; the studies reviewed in mainland Spain go up to 2008. ARIMA models were used to represent their variation. In the preliminary phase of description and identification of the model, a study to detect possible trends in the series was carried out in an isolated manner. Significant trends were found in 15 of the temperature series, and there were trends in precipitation in only five of them. The results obtained for the trends are discussed with reference to those of other, more detailed studies in the different regions, confirming whether the same trend was maintained over time. With the ARIMA models obtained, 12-month predictions were made by measuring errors with the observed data. More than 50% of the series of both were modeled. Predictions with these models could be useful in different aspects of seasonal job planning, such as wildfires, pests and diseases, and agricultural crops. [ABSTRACT FROM AUTHOR]

In 2013, a change in copayment rate was introduced in the Basque Country (one year later than in the other regions in Spain), and improvements were made to drug packaging. In 2014, a National Program Against Bacterial Resistance (Spanish abbreviation: PRAN) was approved. The aim of this study is to analyze the impact of change to the copayment rate, the adjustment of drug packaging, and the approval of PRAN on the consumption of antibiotics. Raw monthly data on the consumption of antibiotics (costs, packages, and daily defined doses per thousand people (DID)) were collected from January 2009 to December 2018 in the Basque Country. Counterfactual and intervention analysis (Autoregressive integrated moving average (ARIMA) model) was performed for the total series, disaggregated by group of antibiotics (2019 WHO Access, Watch, and Reserve (AWaRe) Classification) and active substances with the highest cost per prescription (cefditoren and moxifloxacin), the lowest cost per prescription (doxycycline and cloxacillin), and the most prescribed active ingredients (amoxicillin, azithromycin, and levofloxacin). Introduction of copayment led to a 'stockpiling effect' one month before its implementation, equal to 8% in the three consumption series analyzed. Only the adjustment of drug packaging significantly reduced the number of packages dispensed (−12.19%). PRAN approval reduced consumption by 0.779 DID (−4.51%), representing a significant decrease for both 'access' and 'watch' group antibiotics. Despite the delay in implementing changes to copayment, there was a 'stockpiling effect'. With the adjustment of packaging, fewer packs were prescribed but with a higher drug load and price. PRAN approval reduced both the consumption of 'access group antibiotics' (first-line treatment) and 'watch group antibiotics' (second-line treatment). [ABSTRACT FROM AUTHOR]