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Based on a comprehensive dataset of Chinese A-share stocks, we find significant evidence for analysts' anchoring in China. This anchoring behavior exists only when forecasts are lower than the industry median and correlates to a series of analysts' and corporate characteristics. The following market reactions to analyst anchoring are discontinuous: only forecasts without analysts' anchoring achieve higher and positive returns in the next period, while those with anchoring bias have no influence on the following returns. Further analyses based on the earnings response coefficient reveal that analysts' anchoring inhibits earnings information dissemination, and discontinuous stock performance is mainly from professional institutions. Overall, this paper provides evident results about analyst anchoring and its impact, and the findings are beneficial for investors to efficiently use analysts' forecast information. [ABSTRACT FROM AUTHOR]

This paper analyzes the inflation forecast errors over the period 2021Q1-2022Q3 using forecasts of core and headline inflation from the International Monetary Fund World Economic Outlook for a large group of advanced and emerging market economies. The findings reveal evidence of forecast bias that worsened initially and then subsided towards the end of the sample. There is also evidence of forecast oversmoothing, indicating rigidity in forecast revision in the face of incoming information. Focusing on core inflation forecast errors in 2021, four factors provide a potential ex post explanation: a stronger-than-anticipated demand recovery; demand-induced pressures on supply chains; the demand shift from services to goods at the onset of the pandemic; and labor market tightness. Ex ante, we find that the size of the COVID-19 fiscal stimulus packages announced by different governments in 2020 correlates positively with core inflation forecast errors in advanced economies. This result hints at potential forecast inefficiency, but we caution that it hinges on the outcomes of a few, albeit large, economies. [ABSTRACT FROM AUTHOR]

Establishing the corresponding forecasting models based on numerical model that fails to capture gale events is helpful to improve the ability to forecast gales in coastal areas of China. First, we screened for gale events occurred in coastal areas of Qingdao from 2016 to 2019, and established the dataset of gale events that are missed by the forecast with ECMWF (European Center for Medium-range Weather Forecast) high resolution model (hereinafter referred to as EC model). Second, based on support vector machine (SVM), artificial neural network (ANN) and long short-term memory network (LSTM) algorithms, we established the forecast models for gales along the coast of Qingdao, and used these models to revise the wind speed forecasted by the EC model. Finally, after comparative analysis, a model suitable for forecasting gale along the coast of Qingdao, i.e. the forecast model SVM_2 based on SVM algorithm, was selected and its operational forecasting results were ex⁃ amined. Results show that the SVM_2 model has the smallest forecast error for wind speed compared to other models. In order to evaluate the prediction performance of SVM_2 model in terms of the gale events, two gale events occurred in the coast of Qingdao and influenced by differ⁃ ent weather systems are selected to further examine the prediction error of SVM_2 model and EC model for the gale events. The results show that the error of the maximum wind speed forecasted by the SVM_2 model against the observations is significantly smaller than that of the EC model, and the SVM_2 model also has some improvement on the weak gale along the coast of Qingdao compared to the forecast by EC model.

This study proposes a deep-reinforcement-learning (DRL)-based vehicle-to-grid (V2G) operation strategy that focuses on the dynamic integration of charging station (CS) status to refine solar power generation (SPG) forecasts. To address the variability in solar energy and CS status, this study proposes a novel approach by formulating the V2G operation as a Markov decision process and leveraging DRL to adaptively manage SPG forecast errors. Utilizing real-world data from the Korea Southern Power Corporation, the effectiveness of this strategy in enhancing SPG forecasts is proven using the PyTorch framework. The results demonstrate a significant reduction in the mean squared error by 40% to 56% compared to scenarios without V2G. Our investigation into the effects of blocking probability thresholds and discount factors revealed insights into the optimal V2G system performance, suggesting a balance between immediate operational needs and long-term strategic objectives. The findings highlight the possibility of using DRL-based strategies to achieve more reliable and efficient renewable energy integration in power grids, marking a significant step forward in smart grid optimization. [ABSTRACT FROM AUTHOR]

The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), a type of online coupled chemistry-meteorology model (CCMM), considers the interaction between air quality and meteorology to improve air quality forecasting. Meteorological data assimilation (DA) can be used to reduce uncertainty in meteorological field, which is one factor causing prediction uncertainty in the CCMM. In this study, WRF-Chem and three-dimensional variational DA were used to examine the impact of meteorological DA on air quality and meteorological forecasts over the Korean Peninsula. The nesting model domains were configured over East Asia (outer domain) and the Korean Peninsula (inner domain). Three experiments were conducted by using different DA domains to determine the optimal model domain for the meteorological DA. When the meteorological DA was performed in the outer domain or both the outer and inner domains, the root-mean-square error (RMSE), bias of the predicted particulate matter (PM) concentrations, and the RMSE of predicted meteorological variables against the observations were smaller than those in the experiment where the meteorological DA was performed only in the inner domain. This indicates that the improvement of the synoptic meteorological fields by DA in the outer domain enhanced the meteorological initial and boundary conditions for the inner domain, subsequently improving air quality and meteorological predictions. Compared to the experiment without meteorological DA, the RMSE and bias of the meteorological and PM variables were smaller in the experiments with DA. The effect of meteorological DA on the improvement of PM predictions lasted for approximately 58–66 h, depending on the case. Therefore, the uncertainty reduction in the meteorological initial condition by the meteorological DA contributed to a reduction of the forecast errors of both meteorology and air quality. [ABSTRACT FROM AUTHOR]

In this article, a machine‐learning‐based tool for calibrating numerical forecasts of near‐surface air temperature is proposed. The study area covers Poland representing a temperate type of climate with transitional features and highly variable weather. The direct output of numerical weather prediction (NWP) models is often biased and needs to be adjusted to observed values. Forecasters have to reconcile forecasts from several NWP models during their operational work. As the proposed method is based on deterministic forecasts from three short‐range limited‐area models (ALARO, AROME and COSMO), it can support them in their decision‐making process. Predictors include forecasts of weather elements produced by the NWP models at synoptic weather stations across Poland and station‐embedded data on ambient orography. The Random Forests algorithm (RF) has been used to produce bias‐corrected forecasts on a test set spanning one year. Its performance was evaluated against the NWP models, a linear combination of all predictors (multiple linear regression, MLR) as well as a basic Artificial Neural Network (ANN). Detailed evaluation was done to identify potential strengths and weaknesses of the model at the temporal and spatial scale. The value of RMSE of a forecast obtained by the RF model was 11% and 27% lower compared to the MLR model and the best‐performing NWP model respectively. The ANN model turned out to be even superior, outperforming RF by around 2.5%. The greatest improvement occurred for warm bias during the nighttime from July to September. The largest difference in forecast accuracy between RF and ANN appeared for temperature drops inApril nights. Poor performance of RF for extreme temperature ranges may be suppressed by training the model on forecast error instead of observed values of the variable. [ABSTRACT FROM AUTHOR]

The increasing integration of wind power in power systems necessitates the probabilistic assessment of various uncertain factors. In operational planning, modeling short-term scale uncertainties, i.e., wind power forecast errors, plays an important role. In this paper, according to the different forecast values, the corresponding probability distributions of wind power forecast errors are developed using a data-driven manner. Then, the polynomial chaos expansion surrogate is developed to facilitate the probabilistic load margin assessment considering wind power forecast errors. The effectiveness of the forecast error model is verified using the historical data of realistic wind power plants. The results show that the probability distributions of forecast errors vary with the level of forecast values. Moreover, the performance of the polynomial chaos expansion surrogate in estimating probabilistic load margin is validated in the IEEE 30-bus system. The results demonstrate that the versatile forecast error distributions significantly impact the characteristics of load margin. Moreover, the polynomial chaos expansion surrogate can accelerate the load margin assessment compared to the Monter Carlo simulation while retaining the same accuracy.

Since more than 10 years, the Earth system modeling group at GFZ (ESMGFZ) provides effective angular momentum (EAM) functions for Earth orientation parameter assessment on a routinely daily basis. In addition to EAM of the individual Earth's subsystems atmosphere, ocean, and hydrology, the global mass balance is calculated as barystatic sea level variation by solving explicitly the sea-level equation. ESMGFZ provides also 6-day forecasts for all of these EAM products. EAM forecasts are naturally degraded by forecast errors that typically grow with increasing forecast length, but they also show recurring patterns with occasionally higher errors at very short forecast horizons. To characterize such errors which are not randomly distributed in time, we divided the errors into a systematic and a stochastic contribution. In an earlier study, we were able to detect and remove the large systematic fraction occurring in the atmospheric angular momentum (AAM) wind term forecast errors with a cascading forward neural network model, thereby reducing the total forecast error by about 50%. In contrast, we were not able to remove the random error component assed in this study. Nevertheless, we show that machine learning methods are able to predict quasi-daily variations in time variable EAM forecasts error levels. We plan to provide these forecast error estimates along with the deterministic EAM forecast products for subsequent use in, for example, EOP Kalman filter prediction schemes. [ABSTRACT FROM AUTHOR]

The scenario of renewable energy generation significantly affects the probabilistic distribution system analysis. To reflect the probabilistic characteristics of actual data, this paper proposed a scenario generation method that can reflect the spatiotemporal characteristics of wind power generation and the probabilistic characteristics of forecast errors. The scenario generation method consists of a process of sampling random numbers and a process of inverse sampling using the cumulative distribution function. In sampling random numbers, random numbers that mimic the spatiotemporal correlation of power generation were generated using the copula function. Furthermore, the cumulative distribution functions of forecast errors according to power generation bins were used, thereby reflecting the probabilistic characteristics of forecast errors. The wind power generation scenarios in Jeju Island, generated by the proposed method, were analyzed through various indices that can assess accuracy. As a result, it was confirmed that by using the proposed scenario generation method, scenarios similar to actual data can be generated, which in turn allows for preparation of situations with a high probability of occurrence within the distribution system. [ABSTRACT FROM AUTHOR]

This paper is concerned with the estimation of forecast error, particularly in relation to insurance loss reserving. Forecast error is generally regarded as consisting of three components, namely parameter, process and model errors. The first two of these components, and their estimation, are well understood, but less so model error. Model error itself is considered in two parts: one part that is capable of estimation from past data (internal model error), and another part that is not (external model error). Attention is focused here on internal model error. Estimation of this error component is approached by means of Bayesian model averaging, using the Bayesian interpretation of the LASSO. This is used to generate a set of admissible models, each with its prior probability and likelihood of observed data. A posterior on the model set, conditional on the data, may then be calculated. An estimate of model error (for a loss reserve estimate) is obtained as the variance of the loss reserve according to this posterior. The population of models entering materially into the support of the posterior may turn out to be "thinner" than desired, and bootstrapping of the LASSO is used to increase this population. This also provides the bonus of an estimate of parameter error. It turns out that the estimates of parameter and model errors are entangled, and dissociation of them is at least difficult, and possibly not even meaningful. These matters are discussed. The majority of the discussion applies to forecasting generally, but numerical illustration of the concepts is given in relation to insurance data and the problem of insurance loss reserving. [ABSTRACT FROM AUTHOR]

Warm conveyor belts (WCBs) are rapidly ascending air streams associated with extratropical cyclones. WCBs exert a substantial influence on the evolution of the large‐scale midlatitude flow and have previously been related to increased forecast uncertainty in case studies. This study provides a first systematic investigation of the role of WCBs for errors in medium‐range ensemble forecasts in the Atlantic–European region. The study is enabled through a unique data set, allowing for a Lagrangian detection of WCBs in three years of operational ensemble forecasts from the European Centre for Medium‐Range Weather Forecasts. By analysing the relationship between commonly used error metrics of variables that characterise the large‐scale flow and WCBs, the study aims to shed light on the question of the extent to which WCBs act as a source of forecast errors and as an amplifier of pre‐existing errors in a state‐of‐the‐art global operational numerical weather prediction model. We show that forecasts with high WCB activity are on average characterised by an amplified Rossby‐wave pattern and anticyclonic flow anomalies downstream. We find that the forecast skill is generally reduced when the WCB activity is high, and that WCB activity is particularly increased when the error growth is largest. To establish a causal relationship, we employ two composite approaches. The first focuses on the time of largest error growth and the second calculates normalised forecast error fields centered on WCB objects. Both approaches yield a consistent picture: anomalously high errors are initially associated with misrepresentations of an upstream trough. In regions of WCB ascent and outflow, the errors grow rapidly in terms of magnitude and scale and are projected onto the upper‐level large‐scale circulation. We also find indications that WCBs can cause errors even when the upstream flow is well represented. Notwithstanding, evidence is robust for WCBs acting as an amplifier of forecast uncertainty. [ABSTRACT FROM AUTHOR]

With the development of the economy at hand, local fiscal revenue forecasting is becoming increasingly important for policy making and budgetary management. In this paper, based on SVR (Support Vector Regression) model, the method of building a more accurate local fiscal revenue forecasting model is discussed. The study is constructing a local fiscal revenue forecasting model based on the SVR model to improve forecasting accuracy. The PCA method is applied to preprocess the data to reduce multicollinearity and extract the key influence factors. The SVR model is used to forecast, and the appropriate kernel function and model parameters are chosen. The model’s ability to predict accurately is demonstrated by the average prediction error of 2.47% in the short-term and 3.29% in the medium- and long-term forecasts. The PCA-SVR model is more effective in dealing with seasonal and short-term shocks than traditional time series models. The study proved that the SVR model is an effective and powerful tool for predicting local fiscal revenues.

Using conventional weather map data, regional automatic weather station data, FY4 satellite infrared cloud image, radar echo, EC fine grid (0.25°×0.25°) and other multi-model forecast data,the reason for the forecast error of a rainstorm process in Xiaogan City on June 4, 2022 was analyzed. The results showed that the weather process of heavy torrential rain and local heavy rain in Xiaogan City this time mainly affected the middle and low-level shear line, especially the 850 hPa shear line. Under the action of going south, during the transformation process from warm shear line to cold shear line, convective cloud clusters developed in the warm area of warm shear line and convective cloud clusters developed again on cold shear line. This time, the heavy rainstorm in Xiaogan City and the local heavy rainstorm forecast error were due to the forecast error of the EC fine grid model on the large-scale circulation situation such as the northeast vortex, the main impact system such as the low-level shear line, and the precipitation of multiple models with different time effects the forecasts were all small, the forecast consistency of different models at the same time was poor, and the forecasters rely too much on the forecast products of numerical forecast models. [ABSTRACT FROM AUTHOR]

Prediction of random processes is important in the development of systems of predictive diagnostics of the state of objects of electrical engineering, planning of load changes of generating power plants, and construction of regulators as a part of an operator that converts mismatch into control signals. This article considers an extrapolator the parameters of which change in time in order to minimize the prediction error of nonstationary random processes. The extrapolator uses Chebyshev polynomials that are orthogonal to a set of equidistant points with the coefficients of the predictive polynomial obtained by the least-squares method. The number of points on the time axis in which the value of the random process is known before the prediction interval, as well as the degree of the predicting polynomial, change in time depending on a forecast error that took place earlier. The work of the extrapolator is formalized, the algorithm of its functioning is described, and various rules of control of the mechanism of parameter change are considered. The results of simulation experiments testify to the efficiency of using the adaptive extrapolator for prediction of nonstationary random processes. [ABSTRACT FROM AUTHOR]

This paper summarizes the progress and activities of tropical cyclone (TC) operational forecast centers during the last four years (2018–2021). It is part II of the review on TC intensity change from the operational perspective in the rapporteur report presented to the 10th International Workshop on TCs (IWTC) held in Bali, Indonesia, from Dec. 5–9, 2022. Part I of the review has focused on the progress of dynamical model forecast guidance. This part discusses the performance of TC intensity and rapid intensification forecasts from several operational centers. It is shown that the TC intensity forecast errors have continued to decrease since the 9th IWTC held in 2018. In particular, the improvement of rapid intensification forecasts has accelerated, compared with years before 2018. Consensus models, operational procedures, tools and techniques, as well as recent challenging cases from 2018 to 2021 identified by operational forecast centers are described. Research needs and recommendations are also discussed.

The paper suggests a way of stochastic integration of random integrands with respect to fractional Brownian motion with the Hurst parameter H > 1∕2. The integral is defined initially on the processes that are" piecewise" predictable on a short horizon. Then the integral is extended on a wide class of square integrable adapted random processes. This class is described via a mild restriction on the growth rate of the conditional mean square error for the forecast on an arbitrarily short horizon given current observations. On the other hand, a pathwise regularity, such as Hölder condition, etc., is not required for the integrand. The suggested integration can be interpreted as foresighted integration for integrands featuring certain restrictions on the forecasting error. This integration is based on Itô's integration and does not involve Malliavin calculus or Wick products. In addition, it is shown that these stochastic integrals depend right continuously on H at H = 1/2. [ABSTRACT FROM AUTHOR]

The primary focus of smart grid power analysis is on power load forecasting and data anomaly detection. Efficient and accurate power load prediction and data anomaly detection enable energy companies to develop reasonable production and scheduling plans and reduce waste. Since traditional anomaly detection algorithms are typically for symmetrically distributed time series data, the distribution of energy consumption data features uncertainty. To this end, a time series outlier detection approach based on prediction errors is proposed in this paper, which starts by using an attention mechanism-based convolutional neural network (CNN)-gated recursive unit (GRU) method to obtain the residual between the measured value and its predicted value, and the residual data generally conform to a symmetric distribution. Subsequently, for these residual data, a random forest classification algorithm based on grid search optimization is used to identify outliers in the power consumption data. The model proposed in this paper is applied to both classical and real energy consumption datasets, and the performance is evaluated using different metrics. As shown in the results, the average accuracy of the model is improved by 25.2% and the average precision is improved by 17.2%, with an average recall improvement of 16.4% and an average F1 score improvement of 26.8% compared to the mainstream algorithms. [ABSTRACT FROM AUTHOR]

Using original survey data linked with the financial statements of Japanese listed firms, this study presents an ex post evaluation of firms' ex ante subjective uncertainty. Ex ante forecast uncertainty in terms of sales and employment growth is derived from firms' subjective confidence interval around their point forecasts. Ex post forecast error is calculated as the deviation of the realized figures from the point forecasts. The results indicate that ex ante subjective uncertainty has a significant positive association with realized absolute forecast error. The subjective confidence interval for a firm's own business forecast, in comparison with that for macroeconomic variables, is reliable as a measure of uncertainty. These findings indicate that the subjective probability distribution of business outlook captured by firm surveys contains valuable information for measuring economic uncertainty at the micro-level. [ABSTRACT FROM AUTHOR]

This paper is concerned with the estimation of forecast error, particularly in relation to insurance loss reserving. Forecast error is generally regarded as consisting of three components, namely parameter, process and model errors. The first two of these components, and their estimation, are well understood, but less so model error. Model error itself is considered in two parts: one part that is capable of estimation from past data (internal model error), and another part that is not (external model error). Attention is focused here on internal model error. Estimation of this error component is approached by means of Bayesian model averaging, using the Bayesian interpretation of the LASSO. This is used to generate a set of admissible models, each with its prior probability and likelihood of observed data. A posterior on the model set, conditional on the data, may then be calculated. An estimate of model error (for a loss reserve estimate) is obtained as the variance of the loss reserve according to this posterior. The population of models entering materially into the support of the posterior may turn out to be “thinner” than desired, and bootstrapping of the LASSO is used to increase this population. This also provides the bonus of an estimate of parameter error. It turns out that the estimates of parameter and model errors are entangled, and dissociation of them is at least difficult, and possibly not even meaningful. These matters are discussed. The majority of the discussion applies to forecasting generally, but numerical illustration of the concepts is given in relation to insurance data and the problem of insurance loss reserving.

We address the organization of criminal justice forecasting and implications for its use in criminal justice policymaking. We argue that the use of forecasting is relatively widespread in criminal justice agency settings, but it is used primarily to inform decision-making and practice rather than to formulate and test new policy proposals. Using predictive policing and prison population forecasting as our main examples of the range of forecasting methods adopted in criminal justice practice, we describe their uses, how their use is organized, and the implications of the organizational arrangements for the transparent, reviewable, and consensual use of forecasting. We point out that both prison population forecasting and predictive policing have long histories that have led to advances in methodology. Prison population forecasting has generally become embedded in budget decision-making processes that contribute to greater transparency in method and applications. Predictive policing has been less transparent in method and use, partly because the methods are more complicated and rely on larger amounts of data, but it generally has not be used in ways to foster community engagement and build public support. Concerns about the legitimacy of its use persist. [ABSTRACT FROM AUTHOR]

Numerical models of the atmosphere are based on the best theory available. Understandably, the theoretical assessment of errors induced by the use of such models is confounding. Without clear theoretical guidance, the experimental separation of the model-induced part of the total forecast error is also challenging. In this study, the forecast error and ensemble perturbation variances were decomposed. Smaller- and larger-scale components, separated as a function of the lead time, were independent. They were associated with features with completely vs. only partially lost skill, respectively. For their phenomenological description, the larger-scale variance was further decomposed orthogonally into positional and structural components. An analysis of the various components revealed that chaotically amplifying initial perturbation and error predominantly led to positional differences in forecasts, while structural differences were interpreted as an indicator of the model-induced error. Model-induced errors were found to be relatively small. These results confirmed earlier assumptions and limited empirical evidence that numerical models of the atmosphere may be near perfect on the scales they well resolve. [ABSTRACT FROM AUTHOR]

This study explores the controlling factors of the uncertainties and error growth at different spatial and temporal scales in forecasting the high-impact extremely heavy rainfall event that occurred in Zhengzhou, Henan Province China on 19–20 July 2021 with a record-breaking hourly rainfall exceeding 200 mm and a 24-h rainfall exceeding 600 mm. Results show that the strengths of the mid-level low-pressure system, the upper-level divergence, and the low-level jet determine both the amount of the extreme 24-h accumulated and hourly rainfall at 0800 UTC. The forecast uncertainties of the accumulated rainfall are insensitive to the magnitude and the spatial structure of the tiny, unobservable errors in the initial conditions of the ensemble forecasts generated with Global Ensemble Forecast System (GEFS) or sub-grid-scale perturbations, suggesting that the predictability of this event is intrinsically limited. The dominance of upscale rather than upamplitude error growth is demonstrated under the regime of k−5/3 power spectra by revealing the inability of large-scale errors to grow until the amplitude of small-scale errors has increased to an adequate amplitude, and an apparent transfer of the fastest growing scale from smaller to larger scales with a slower growth rate at larger scales. Moist convective activities play a critical role in enhancing the overall error growth rate with a larger error growth rate at smaller scales. In addition, initial perturbations with different structures have different error growth features at larger scales in different variables in a regime transitioning from the k−5/3 to k−3 power law. Error growth with conditional nonlinear optimal perturbation (CNOP) tends to be more upamplitude relative to the GEFS or sub-grid-scale perturbations possibly owing to the inherited error growth feature of CNOP, the inability of convective parameterization scheme to rebuild the k−5/3 power spectra at the mesoscales, and different error growth characteristics in the k−5/3 and k−3 regimes. [ABSTRACT FROM AUTHOR]

From July 16 to 17, 2021, a quasi-stationary meso-β-scale vortex (MβV) was developed in the middle and lower reaches of the Yangtze River, causing localized heavy rainfall, thunderstorms, and gales. The ECMWF control forecast exhibited obvious northward bias of the MβV as well as large errors in the precipitation forecast. In this study, two recently proposed metrics-the Measure of Forecast Challenge and the Predictability Horizon Diagram Index-were applied to analyze the forecast uncertainty of the MβV and its associated precipitation, and the reason for model bias was discussed thereby. The results show that: (1) The convergence of the low-level southwest and southeast streams on the east side of the vortex and the horizontal vorticity transport of the low-level jet are the main dynamical factors for the development of the vortex, while the latent heat release of MβV associated precipitation is the main thermal factor. (2) EC control forecasts with different initialization time show northward bias of the MβV as well as the rainbelt, and the dispersion of EC ensemble forecasts is not consistent with observation, indicating the low predictability of this event. (3) The overestimation of v component of the low-level southwest stream south of the MβV and the northward error of the convergence zone east of the MβV lead to a northward bias of precipitation forecast. After that, the positive feedback mechanism between latent heat release, low-level jet, and the MβV causes a further northward bias and finally leads to the marked error in the forecast by EC during this event. [ABSTRACT FROM AUTHOR]

Accurate runoff forecast is very important for reservoir operation. In view of the shortcomings of the existing correction models for runoff forecast, including the influence of the difference of external factors on the forecast results is not considered, and the optimal situation adaptation of different forecast models is not considered, three models, i.e., long and short-term memory neural network model (LSTM), gaussian process regression model (GPR) and support vector machine regression model (SVR), are used to forecast the relative errors of runoff forecast under different scenarios in this paper. The classification of forecast scenarios is determined based on factors such as rainfall, inflow, and foresight period, and two scenario sets are given, i.e., 12 forecast scenarios and 24 forecast scenarios. Then, a multi-model coupled runoff forecast correction method considering forecast error and forecast scenario is proposed. Through the case study of the Three Gorges Reservoir (TGR), it is found that, when the analysis is carried out based on the forecast period, the SVR model should be used for forecast correction when the foresight period is 1–5 days, and the LSTM model should be used for forecast correction when the foresight period is 6 days. The application effect of SVR and LSTM is better than GPR in the scenario set of 12 forecast scenarios. LSTM model has the highest accuracy of forecast correction in the scenario set of 24 forecast scenarios, and the mean value of the coefficient of certainty (R2) changes from 0.919 of 12 forecast scenarios to 0.931 of 24 forecast scenarios, increasing by 1.31%. The mean value of mean relative error (MRE) changes from 6.80% of 12 forecast scenarios to 5.64% of 24 forecast scenarios, a decrease of 17.06%. Finally, the best model adaptation table corresponding to different forecast scenarios of TGR is established, which has an important guiding role in the actual runoff forecast of TGR. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this study is to examine whether and how analysts’ foreign ancestral origins would have an effect on analysts’ earning forecasts in particular and ultimately on firms’ information environment in general. Design/methodology/approach By inferring analysts’ ancestral countries based on their surnames, this study empirically examines whether analysts’ ancestral countries affect their earnings forecast errors. Findings Using novel data on analysts’ foreign ancestral origins from more than 110 countries, this study finds that relative to analysts with common American surnames, analysts with common foreign surnames tend to have higher earnings forecast errors. The positive relation between analyst foreign surnames and earnings forecast errors is more likely to be observed for African-American analysts and analysts whose ancestry countries are geographically apart from the USA. In contrast, this study finds that when analysts’ foreign countries of ancestry are aligned with that of the CEOs, analysts exhibit lower earnings forecast errors relative to analysts with common American surnames. More importantly, the results show that firms followed by more analysts with foreign surnames tend to exhibit higher earnings forecast errors. Originality/value Taken together, findings of this study are consistent with the conjecture that geographical, social and ethnical proximity between managers and analysts affect firms’ information environment. Therefore, this study contributes to the determinants of analysts’ earnings forecast errors and adds to the literature on firms’ information environment.

Many studies have evaluated the value of long-term inflow forecast for hydropower operation in high-precision watersheds and explored the impact of forecast uncertainty on hydropower benefits. However, few of them focuses on the low-accuracy regions and the specific impact of forecast errors on hydropower benefits. Taking Hunjiang cascaded system in China as an example, this study investigated the value of low accuracy long-term inflow forecast for hydropower operation, revealed the influence mechanism of forecast errors on hydropower benefits, and determined the critical threshold for accuracy of beneficial forecast. Results show that low accuracy forecast but higher than critical threshold can increase hydropower generation. Hunjiang has a low accuracy long-term forecast with qualified rate being 30–40 %, but annual hydropower generation can be increased by 14.86–29.58 million kWh. The influence mechanism of forecast errors on hydropower operation presents three typical scenarios, two of which are harmless. One is that the error tolerance of operation rules can avoid some decision-making errors; the other one is that when current misreporting is consistent with the inflow in a longer lead time, the misreporting may instead be beneficial. We conclude that low accuracy long-term forecast is valuable but higher accuracy brings higher benefits. • Low accuracy long-term inflow forecast improves hydropower benefits. • The influence mechanism of forecast errors on hydropower benefits is investigated. • The reason why low accuracy forecast can improve hydropower benefits is explained. [ABSTRACT FROM AUTHOR]

Numerical weather prediction models exhibit errors while simulating atmospheric processes. To provide alerts for weather hazards, early warning systems are fed with forecast data from these models. The success of such an early warning system requires the minimization of errors that are induced by the forecast models. On the other hand, machine learning techniques have been proposed as an alternate method for nonlinear and dynamic systems due to the fact that this approach includes effective structure and parameter estimation methodologies, and it is powerful when implemented for problems whose resolutions require knowledge that is hard to specify. In this study, the goal is to implement machine learning methods as post-process algorithms on model output. The algorithm will discover the patterns that produce the errors and then lead to improved information for the system. This way, better planning and more efficient decision making are possible. High-resolution forecast data are available from Weather Research and Forecasting Model (WRF) simulations using initial and boundary conditions from the Global Forecasting System (GFS). Using nested domains, the desired downscaling can be achieved. Observations are available from General Aviation Applications 3D S.A.’s automatic weather station network, which has been operational for over 5 years. The network covers the region of Central Macedonia and has more than twenty stations. Ten of them were selected based on the availability of the data and the data quality control checks. Two sets of data are established. The first one is used to train the algorithm and the other to validate the performance of the new forecast.

The article considers the issue of making forecasts of the main indicators of the activities of airlines for their effective functioning in the air transportation market. It describes the life cycle of airlines and provides a classification of the forecasts that are currently being made. The interrelation of correlations of the key parameters of the airline’s functioning is considered, the use of correlations in forecasting is shown, actual calculations are presented, and a significant increase in forecast accuracy when using this forecasting method is demonstrated.

This study examines the relevance of integrated reporting quality (IRQ) to capital markets. We investigate whether IRQ benefits capital market participants by improving a firm's information environment, using analyst earnings forecast accuracy as a proxy. Our study focuses specifically on companies that publish integrated reports on a voluntary basis. Based on a scoring model, we assess IRQ and its effects with data from 2015 to 2019 of 101 companies. The results indicate no significant relationship between IRQ and analyst earnings forecast accuracy. Thus, IRQ does not appear to improve a firm's information environment, at least not currently in a voluntary setting. Drawing on previous literature in the field, this study further concludes that integrated reporting (IR) in general has not yet reached its full potential in benefitting capital markets. Potential implications of our results are that the standard setters should work to improve the specificity and rigor of their guidelines, and analysts should become more involved in developing IR guidelines to make them more relevant to their information needs. IR seems to unfold its benefits better in mandatory settings, which could call for regulators to make IR mandatory. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Economics Business & Finance Research / Ekonomi İşletme ve Maliye Araştırmaları Dergisi is the property of Irfan Ersin 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.)

Although tropical cyclone (TC) track forecast errors (TFEs) of operational warning centres have substantially decreased in recent decades, there are still many cases with large TFEs. The International Grand Global Ensemble (TIGGE) data are used to study the possible reasons for the large TFE cases and to compare the performance of different numerical weather prediction (NWP) models. Forty-four TCs in the western North Pacific during the period 2007–2014 with TFEs (+24 to +120 h) larger than the 75th percentile of the annual error distribution (with a total of 93 cases) are identified.Four categories of situations are found to be associated with large TFEs. These include the interaction of the outer structure of the TC with tropical weather systems, the intensity of the TC, the extension of the subtropical high (SH) and the interaction with the westerly trough. The crucial factor of each category attributed to the large TFE is discussed.Among the TIGGE model predictions, the models of the European Centre for Medium-Range Weather Forecasts and the UK Met Office generally have a smaller TFE. The performance of different models in different situations is discussed.

Copyright of Accounting Perspectives is the property of Canadian Academic Accounting Association 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.)

The link between cost of capital and information risk is one of the most fundamental and controversial issues in financial accounting. We need an exogenous shock to unequivocally tease out the link between information risk and cost of capital (CoC). In this study, we consider a firm's listing with the S&P 500 index as the exogenous shock. Listing with the index is expected to bring the firm into the limelight, leading to increased scrutiny of the firm and its management performance by market participants and stock analysts. This may expose the firm to increased information risk, which may adversely affect its cost of capital in the short run. Empirical evidence in this study suggests firms that are added to the S&P 500 index exhibit higher cost of capital over the first year after inclusion compared to a carefully constructed matched sample of non-S&P 500 stocks. Results also indicate that the volume of information released and precision of information once a firm is listed with the index significantly impact its cost of capital. [ABSTRACT FROM AUTHOR]

I study yearly changes in personal well-being combining data on current, retrospective and prospective life satisfaction from the German Socio-Economic Panel. Predicted and remembered changes in life satisfaction are both positive on average and match well, whereas the average year to year-change inferred from reports of current life satisfaction is negative. Retrospective assessments of past well-being are strongly influenced by current life satisfaction, significantly related to past life satisfaction and linked to past predictions of current satisfaction. Due to different problems related to the ordinal measurement scale, changes in subjective reference systems and recall ability, the analysis overall suggests that direct reports of intertemporal changes provide valuable additional information for the analysis of individual well-being. [ABSTRACT FROM AUTHOR]

The primary focus of smart grid power analysis is on power load forecasting and data anomaly detection. Efficient and accurate power load prediction and data anomaly detection enable energy companies to develop reasonable production and scheduling plans and reduce waste. Since traditional anomaly detection algorithms are typically for symmetrically distributed time series data, the distribution of energy consumption data features uncertainty. To this end, a time series outlier detection approach based on prediction errors is proposed in this paper, which starts by using an attention mechanism-based convolutional neural network (CNN)-gated recursive unit (GRU) method to obtain the residual between the measured value and its predicted value, and the residual data generally conform to a symmetric distribution. Subsequently, for these residual data, a random forest classification algorithm based on grid search optimization is used to identify outliers in the power consumption data. The model proposed in this paper is applied to both classical and real energy consumption datasets, and the performance is evaluated using different metrics. As shown in the results, the average accuracy of the model is improved by 25.2% and the average precision is improved by 17.2%, with an average recall improvement of 16.4% and an average F1 score improvement of 26.8% compared to the mainstream algorithms.

The paper is based on research that identified and analyzed the effects of the variable context of the period 2011-2019 on the entrepreneur's businesses from 16 states. The database used in this research has been selected from the Global Entrepreneurship Monitor. The data were obtained based on surveys involving adult entrepreneurs. The aim of the research was to identify the characteristics and motivations of entrepreneurs in selected states to start new businesses. The data set includes variables that reflect the entrepreneurial intentions of individuals, their abilities, opportunities, and risks depending on gender. The research was conducted using random and fixed effects models. Within the research methodology, the final analysis model selected explains 95% (R2) of the variation of TEA (total early-stage entrepreneurial activity). The research results show a high degree of heterogeneity in the profile of entrepreneurs. Research has shown that fear of failure has positive effects on TEA. During the research, we tested the predictive power of the fixed effects model, and the results showed that it is very accurate. The results of our research contribute to shaping a well-defined profile of the entrepreneur's behavior from different states. [ABSTRACT FROM AUTHOR]

To improve the adaptability for renewable energy sources (RES), a multitime scale optimal dispatch method, which considers the multiple correlations of RES, is proposed in the active distribution network (ADN). The proposed method includes a multiple correlation model of RES and a scheduling framework. The multiple correlation model of RES is formulated by t Copula function, Sklar's theorem, and conditional distribution of multivariate t distribution. The multiple correlation model can estimate the forecast error of the future period according to the historical data and the latest prediction. The scheduling framework further employs the revised forecast to coordinate the fast/slow-response regulation resources in the ADN under three timescales. In the day-ahead stage, the operation state of slow-response resources is optimized and determined. In the intraday stage, the output of the fast-response resource, which is transferred to the real-time stage as reference trajectory, is optimized based on the short-term prediction. In the real-time stage, the stochastic model predictive control is adopted to adjust the output of fast-response resources based on ultra-short-term prediction and reference trajectory. Case analyses indicate that the proposed method can effectively reduce the voltage fluctuation, promote the utilization of demand-side resources, and improve the system economic performance by introducing the multiple correlation model. [ABSTRACT FROM AUTHOR]

This paper examines the relation between the quality of forecasts and the types of analysts' stock recommendations. Using hand-collected data on stock recommendations issued for companies listed on the Warsaw Stock Exchange (WSE) in the period 2005–2012, we find that, despite analysts' clear tendency to issue positive stock recommendations, this tendency generally does not affect the quality of their forecasts in all types of stock recommendations. The accuracy of forecasts, however, decreases as the complexity of the forecasted items (revenue, EBIT, net income, and FCF) increases. We also find no clear difference in the level of analysts' optimism (pessimism) in their forecasts between different types of stock recommendations, but analysts' optimism is visible in the size of their forecasts' overestimation. The findings can help in assessing the value of the stock recommendation on the WSE. [ABSTRACT FROM AUTHOR]

In this work, the use of adaptive filters for reducing forecast errors produced by a Regional Climate Model (RCM) is investigated. Seasonal forecasts are compared against the reanalysis data provided by the National Centers for Environmental Prediction. The reanalysis is used to train adaptive filters based on the Recursive Least Squares algorithm in order to reduce the forecast error. The K-means unsupervised learning algorithm is used to obtain the number of filters to employ from the climate variables. The proposed approach is applied to some climate variables such as the meridional wind, zonal wind, and the geopotential height. The forecast is produced by the Eta RCM at 40-km resolution in a domain covering most of Brazil. Results show that the proposed approach is capable of reducing the forecast errors, according to evaluation metrics such as normalized mean square error, maximum absolute error, and maximum normalized absolute error, thus improving the seasonal climate forecasts. [ABSTRACT FROM AUTHOR]

Professional forecasters of economic data are remunerated based on accuracy and positive publicity generated for their firms. This remuneration structure incentivizes them to stick to the median forecast but also to make bold forecasts when they perceive to have superior private information. We find that skewness in the distribution of expectations, potentially created by bold forecasts, predicts economic surprises across a wide range of US economic indicators in‐sample and out‐of‐sample, confirming our hypothesis that forecasters behave strategically and possess private information. This strategic bias found in US economic forecasts is also exhibited in individual forecasters' data as well as in continental Europe, the United Kingdom, and Japan. We show that it has been increasing both through time and in relation to the behavioral anchor bias. Our results suggest that the pervasiveness of the biases depends on the popularity of the economic indicator being released, both in the United States and internationally. [ABSTRACT FROM AUTHOR]