Reliable Forecasts Research Articles

A new framework for ultra-short-term electricity load forecasting model using IVMD–SGMD two–layer decomposition and INGO–BiLSTM–TPA–TCN

Accurate forecasting of electricity loads is crucial for the development of electricity scheduling and supply services. With the increase in distributed electricity energy sources and the complexity of electricity systems, the strong volatility of load changes brings more challenges to the reliability of load forecasting. Therefore, we propose a novel ultra-short-term electricity load forecasting model using improved two-layer decomposition and an improved deep learning model with temporal pattern attention based on improved northern goshawk optimization (INGO). First, the load data were decomposed thoroughly using the two-layer decomposition model of improved variational mode decomposition (IVMD) with parameter optimization by INGO and symplectic geometry mode decomposition (SGMD) to improve the interpretability of the subsequences. Subsequently, INGO is used to optimize the parameters in bidirectional long short-term memory (BiLSTM). Temporal pattern attention (TPA) is added to BiLSTM, which extracts complex relationships from the hidden neurons of BiLSTM and selects relevant information from different time scales. After predicting and reconstructing the subsequences, the temporal convolutional network (TCN) prediction model is used to perform error correction to improve the final prediction accuracy. Because many government reports and policy information are summarized and published quarterly, to provide information support, we divide the electricity load datasets of two countries by quarters before forecasting. By performing multiple sets of experiments on the two datasets, it is demonstrated that the proposed model has high precision and robustness, and the obtained ultra-short-term electricity load forecasting results can accurately fit the load fluctuation trend.

Synergizing Convergent Cross-Mapping and machine learning for reliable daily forecasting of riverine chlorophyll-a concentration

River algal blooms pose a global ecological and environmental problem, resulting in serious consequences for watershed ecosystems and human health. However, current research has yet to fully identify factors influencing river algal blooms or accurately predict chlorophyll-a concentration, a key indicator, at various lead-times using high-frequency data. Here, we employed Empirical Dynamic Modeling (EDM) and machine learning techniques to forecast daily chlorophyll-a concentration in the Han River of China. The Convergent Cross-Mapping (CCM) analysis revealed the causal relationships between chlorophyll-a concentration and eight variables: pH, total nitrogen, water level in the Han River, water level in the Yangtze River, water temperature, permanganate index, sunlight duration and total phosphorus in the Han River. Subsequent multivariate EDM models with three lead-times (i.e., 1-day, 5-day, and 10-day) only showed acceptable performance in model training (R2 = 0.19–0.73, MAE = 2.76–9.17 μg/L), while exhibited poor predictive performance in model testing (R2 = − 0.50–0, MAE = 9.64–13.09 μg/L). However, Gradient Boosting Machine (GBM) and Random Forest (RF) models with three lead-times exhibited robust performance in both model training (R2 ≥ 0.8, MAE < 4 μg/L) and testing (R2 > 0.6, MAE < 6 μg/L), demonstrating that machine learning models were more suitable than multivariate EDM models for reliably predicting algal blooms. Our study contributes valuable tools for predicting daily chlorophyll-a concentration. The methods presented herein hold broad applicability and offer insights into predicting the entire process of river algal blooms based on daily monitoring data.

Turkey’s Hydropower Potential in the Near Future and the Possible Impacts of Climate Change—A Case Study of the Euphrates–Tigris Basin

Hydropower is becoming an important renewable energy source in Turkey, but the ever-changing atmospheric and climatic conditions of Turkey make it very difficult to be projected efficiently. Thus, an efficient estimation technique is crucial for it to be adopted as a reliable energy source in the future. This study evaluates Turkey’s hydropower potential in the Euphrates–Tigris Basin under changing climatic conditions. We adapted an empirical equation to model reservoir outflows, considering the site-specific characteristics of 14 major dams. Initial results from employing a model with a constant empirical coefficient, α, yielded moderate predictive accuracy, with R2 values ranging from 0.289 to 0.612. A polynomial regression identified optimal α values tailored to each dam’s surface area, significantly improving model performance. The adjusted α reduced predictive bias and increased R2 values, enhancing forecast reliability. Seasonal analysis revealed distinct hydropower trends: Ataturk Dam showed a notable decrease of 5.5% in hydropower generation up to 2050, while Birecik and Keban Dams exhibited increases of 2.5% and 2.2%, respectively. By putting these discoveries into practice, water resource management may become more robust and sustainable, which is essential for meeting Turkey’s rising energy needs and preparing for future climatic challenges. This study contributes valuable insights for optimizing reservoir operations, ensuring long-term hydropower sustainability, and enhancing the resilience of water resource management systems globally.

Statistical Postprocessing of Week-1 and Week-2 Precipitation Forecasts over Taiwan

Abstract The predictability of precipitation is hindered by finer-scale processes not captured explicitly in global numerical models, such as convective interactions, cloud microphysics, and boundary layer dynamics. However, there is growing demand across various sectors for medium- (3–10-day) and extended-range (10–30-day) quantitative precipitation forecasts (QPFs) and probabilistic QPFs (PQPFs). This study uses a novel statistical postprocessing technique, APPM, that combines analog postprocessing (AP) with probability matching (PM) to produce week-1 and week-2 accumulated precipitation forecasts over Taiwan. AP searches for historical predictions that closely resemble the current forecast and create an AP ensemble using the observed high-resolution precipitation patterns corresponding to these forecast analogs. Frequency counting and PM are then separately applied to the AP ensemble to produce calibrated and downscaled PQPFs and bias-reduced QPFs, respectively. Evaluation over a 22-yr (1999–2020) period shows that raw ensemble forecasts from the GEFS of NOAA/NWS/Environmental Modeling Center, collected for the subseasonal experiment, are underdispersive with a wet bias. In contrast, the AP ensemble spread well represents forecast uncertainty, leading to substantially more reliable and skillful probabilistic forecasts. Furthermore, the AP-based PQPF demonstrates superior discrimination ability and yields notably greater economic benefits for a wider range of users, with the maximum economic value increasing by 30%–50% for the week-2 forecast. Compared to the raw ensemble mean forecast, the calibrated QPF exhibits lower mean absolute error and explains 3–8 times more variance in observations. Overall, the APPM technique significantly improves week-1 and week-2 QPFs and PQPFs over Taiwan. Significance Statement There are two significant challenges in improving precipitation forecasts beyond a few days in Taiwan. First, large-scale numerical models often struggle with accurately predicting precipitation locations, magnitudes, and providing sufficient detail. Second, probabilistic precipitation forecasts have been unreliable, failing to convey accurate uncertainty information to users. In response to these challenges, this study has developed a relatively simple yet effective technique that corrects the spatiotemporal distribution of predicted precipitation and downscales the forecasts from a 1° to 1-km spatial resolution. Our results demonstrate that this technique significantly alleviates these two issues, resulting in more accurate precipitation forecasts and more reliable probabilistic precipitation forecasts within a 2-week timeframe.

Physics-Informed Deep-Learning Models Improve Forecast Scalability, Reliability

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 4042557, “Physics-Informed Deep-Learning Models for Improving Shale and Tight Forecast Scalability and Reliability,” by Kainan Wang, SPE, Lichi Deng, and Yuzhe Cai, SPE, Chevron, et al. The paper has not been peer reviewed. _ In the complete paper, the authors present a workflow that combines probabilistic modeling and deep-learning models trained on an ensemble of physics models to improve scalability and reliability for shale and tight reservoir forecasting. Their approach is applied to synthetic cases and many wells in the Permian Basin. Through hindsight studies, these models have been demonstrated to generate realistic and diverse production curves, capture the physics of unconventional flow, quantify well-production-outlook uncertainty, and help interpretation of subsurface uncertainty. Introduction In the realm of unconventional asset development, scalable forecasting is a key component in forecast reliability. In recent years, data-driven machine-learning models and workflows have emerged as potent tools for predicting well performance, particularly in scenarios where wells share similar reservoir properties, completion designs, and operational conditions. A novel approach known as physics-informed machine learning (PIML) has gained prominence. These models leverage the strengths of machine learning while honoring the constraints imposed by physical laws. By incorporating observational, inductive, or learning bias, they bridge the gap between empirical data and fundamental physics. In this paper, the authors showcase the application of a PIML system tailored specifically for unconventional reservoirs. Their methodology involves the selection of an appropriate physics modeling framework, coupled with the development of multiple machine-learning models that augment the limited field data set. Data Acquisition and Generation Bottomhole pressure (BHP) data are key to reliable forecasting because they reflect constraints to well production. Downhole pressure gauges can provide measurements of BHP accurately, but these measurements are not always available. Numerical models can be used to correlate surface measurements to BHP with some accuracy, taking into consideration the well configuration and fluid properties, yet it is too time-consuming to develop such models for many wells in relation to the speed needed for unconventional reservoir development. An in-house machine-learning model was developed for BHP calculation to further augment the data set of gauge measurements and numerical modeling. Through a workflow that chooses the wells with higher-fidelity data as a training set, and careful calibration and iterations between incrementally adding wells of high-fidelity data to reduce uncertainty, the trained machine- learning model is able to predict BHP accurately for many more wells. Another type of engineering data important for reliable well-performance analysis and forecasting is reservoir-fluid description. In this study, the authors leveraged an internally developed pressure/volume/temperature (PVT) machine-learning model trained on proprietary PVT data, which was also shown to provide reliable predictions to key PVT properties such as saturation pressure, formation volume factor, and viscosity from limited inputs at the reservoir and separator level. These PVT models not only enhance scalability of any downstream forecasting models but also play a crucial role in physics-informed uncertainty analysis for these models.

Data-driven forecasting framework for daily reservoir inflow time series considering the flood peaks based on multi-head attention mechanism

Accurate and reliable daily reservoir inflow forecast plays an essential role in several applications involving the management and planning of water resources, such as hydroelectric generation, flood control, water supply, and basin ecological dispatching. Runoff usually exhibits strong non-linearity, high uncertainty, and spatial and temporal variability. Existing techniques fail to capture complete dynamics change processes effectively. A data-driven forecasting framework for daily reservoir inflow time series considering the flood peaks based on a multi-head attention mechanism was developed, referred to as the GWOCS-VMD-CNN-Transformer (GCVCT). First, the model utilize Grey Wolf Optimizer coupled with Cuckoo Search (GWO-CS) algorithms to optimize parameters in variational mode decomposition model (VMD). This approach helps obtain highly correlated intrinsic mode function (IMF) components, enhancing the frequency resolution of the input dataset. The proposed method overcomes the bottleneck of other available methods by decomposing the time series to capture the main long-term and short-term properties of hydrological processes. Second, the convolution neural network and Transformer (CNN-Transformer) are based on a multi-head attention mechanism as the objective predictive method. Finally, six evaluation indicators verify the performance of the proposed approach. The approach’s reliability was evaluated using the historical daily reservoir inflow data from the Xiluodu (XLD) and Wudongde (WDD) reservoirs in the Jinsha River Basin, China. Several single and hybrid models were developed for comparative analysis. The results indicate that the proposed ensemble approach fits better than other developed model methods. The GCVCT model showed excellent performance in forecasting the inflows of XLD and WDD reservoirs, with NSE values of 0.985 and 0.984, respectively. Furthermore, the GCVCT framework forecast capacity for peak inflow was further verified through discussion and analysis of the 48 peak flows during the validation period, consistently outperforming other models in predicting peak flow for both study reservoirs. This framework provides an effective method for the scientific optimal scheduling of hydropower reservoirs, enabling more sustainable and efficient management practices. It also demonstrates the potential of powerful deep-learning models in intelligent hydrological forecasting.

Prediction of interface morphology formed by the oblique gas jet impinging on a liquid surface

This study presents a theoretical analysis of the oblique gas jet impingement process on a liquid surface, elucidating the evolution of the flow field distribution and the influence of jet parameters on cavity shape dynamics. By integrating surface tension effects, the existing Blanks and Chandrasekhara model was refined to develop an advanced predictive model for cavity morphology. The theoretical framework was substantiated through numerical simulations and corroborated with experimental measurements of cavity dimensions, captured using state-of-the-art machine vision technology. The findings reveal a consistent trend in cavity dimension variations: an increase in the cavity surface width with the elevation of the impinging angle from the vertical and an escalation in gas jet velocity. Conversely, a reduction in the impinging angle coupled with an increase in jet velocity leads to a deeper cavity. To enhance the predictive accuracy, the model underwent iterative optimization, incorporating experimental data and accounting for jet parameters. The refined model demonstrated achieved a maximum error of 0.135 mm and a minimum error of 0.03 mm, providing reliable forecasts of cavity depth, which is pivotal for applications in fluid dynamics and related engineering fields.

Deep-learning and data-resampling: A novel approach to predict cyanobacterial alert levels in a reservoir

The proliferation of harmful algal blooms results in adverse impacts on aquatic ecosystems and public health. Early warning system monitors algal bloom occurrences and provides management strategies for promptly addressing high-concentration algal blooms following their occurrence. In this study, we aimed to develop a proactive prediction model for cyanobacterial alert levels to enable efficient decision-making in management practices. We utilized 11 years of water quality, hydrodynamic, and meteorological data from a reservoir that experiences frequent harmful cyanobacterial blooms in summer. We used these data to construct a deep-learning model, specifically a 1D convolution neural network (1D-CNN) model, to predict cyanobacterial alert levels one week in advance. However, the collected distribution of algal alert levels was imbalanced, leading to the biased training of data-driven models and performance degradation in model predictions. Therefore, an adaptive synthetic sampling method was applied to address the imbalance in the minority class data and improve the predictive performance of the 1D-CNN. The adaptive synthetic sampling method resolved the imbalance in the data during the training phase by incorporating an additional 156 and 196 data points for the caution and warning levels, respectively. The selected optimal 1D-CNN model with a filter size of 5 and comprising 16 filters achieved training and testing prediction accuracies of 97.3% and 85.0%, respectively. During the test phase, the prediction accuracies for each algal alert level (L-0, L-1, and L-2) were 89.9%, 79.2%, and 71.4%, respectively, indicating reasonably consistent predictive results for all three alert levels. Therefore, the use of synthetic data addressed data imbalances and enhanced the predictive performance of the data-driven model. The reliable forecasts produced by the improved model can support the development of management strategies to mitigate harmful algal blooms in reservoirs and can aid in building an early warning system to facilitate effective responses.

Operational SYM‐H Forecasting With Confidence Intervals Using Deep Neural Networks

AbstractIn this study, we develop a robust real‐time forecast system for the SYM‐H index using Deep Neural Networks and real‐time Solar Wind measurements along with Interplanetary Magnetic Field parameters. This system provides not only one‐off forecasts but also quantile‐based confidence intervals, offering a range within which the observed SYM‐H values are expected to fall, enhancing forecast reliability and usability. The model is tested both on historical level 2 science‐ready data and on preliminary observations, which are closer to the operational environment that the model is expected to work on, demonstrating the model's robustness and practical utility in real‐time scenarios. The integration of quantile forecasts into SYM‐H prediction models represents a significant advancement, providing decision‐makers with more accurate and trustworthy information to manage the hazard of geomagnetic storms.

СОВРЕМЕННОЕ СОСТОЯНИЕ СИСТЕМЫ ГИДРОЛОГИЧЕСКИХ НАБЛЮДЕНИЙ

The article considers the key aspects of optimization of the composition and placement of the hydrological network of the Republic of Kazakhstan in the context of ensuring sustainable monitoring of water resources. The main attention is paid to the preservation of reference (long-row) observation points necessary for territorial generalizations, development of methods for hydrological calculations and forecasts, as well as assessment of long-term changes in the hydrological regime in the context of climate change. The scientific study emphasizes the importance of increasing the number of information posts to improve the reliability of hydrological forecasts and warnings of hazardous phenomena, equipping the hydrological network with modern equipment in strategically important areas, and restoring specialized observation points for experimental research. The implementation of the proposed measures will meet the current and future needs of the economy for hydrological information, as well as improve the accuracy and reliability of hydrological calculations and forecasts.

FORECASTING AND OPTIMIZATION OF CATALYTIC CRACKING UNIT OPERATION UNDER CONDITIONS OF FUZZY INFORMATION

This paper discusses the application of nonlinear regression to forecast and optimize the operation of catalytic cracking units under conditions of fuzzy information. Catalytic cracking is a crucial process in oil refining that produces high-quality gasoline and other light hydrocarbon products. However, the complexity of the process and the uncertainty of initial data complicate the modeling and optimization of plant operations. To address this issue, a nonlinear regression method is proposed that accommodates the fuzziness of input and output parameters described by linguistic variables. The methodology includes the collection and formalization of expert knowledge, the construction of fuzzy models, and their integration into the process control system. Forecasting is performed by creating regression models that describe the relationships between operational parameters and product quality characteristics. The paper presents a procedure for developing and applying nonlinear regression models, describes algorithms for synthesizing linguistic models, and provides examples of their use to optimize the operation of catalytic cracking units. The modeling results demonstrate the high adequacy and accuracy of the proposed method, as well as its advantages over traditional approaches in conditions of uncertainty and data scarcity. The scientific novelty of the research lies in the development and testing of advanced nonlinear regression models adapted for analyzing and optimizing catalytic cracking processes based on fuzzy data. These methods take into account the specificity and uncertainty of process data, improving the accuracy and reliability of forecasts, which facilitates more effective management of production processes in the petrochemical industry. The main reason for conducting this study is the need to improve the control of oil refining processes, particularly catalytic cracking, which plays an important role in producing high-quality gasoline. The complexity of this process and the presence of fuzzy information caused by fuzzy initial data require the development of new modeling and optimization methods. Existing traditional models based on deterministic methods are often insufficient under uncertainty. This leads to a decrease in the accuracy of process control, which can negatively affect the quality of the final product and production efficiency. The use of nonlinear regression in combination with fuzzy logic is a more flexible and adaptive approach that allows you to take into account the fuzziness and uncertainty of data and use expert knowledge to build models that match the actual operating conditions of the units. Thus, this study aims to solve the key problems associated with data uncertainty and the complexity of the catalytic cracking process, which will improve the accuracy of forecasting and optimization of the units. The main contribution is creating a model that uses nonlinear regression methods in combination with fuzzy logic. This allows uncertainty in input data (such as reactor temperature or pressure) to be effectively considered and processed to improve gasoline and other product yield forecasts. It is shown that using nonlinear regression combined with fuzzy logic significantly improves the management of technological processes, increases the output and quality of products, and reduces production costs. The conclusion of the paper discusses the prospects for further development of the methodology and its application to solve similar tasks in other areas of chemical technology.

Short term load forecasting analysis using machine learning method: An SVM based study

Short-Term Load Forecasting (STLF) is crucial for effective energy management, enabling utilities to optimize electricity generation, distribution, and pricing strategies. This study explores the application of three machine learning models—Linear Regression (LR), Artificial Neural Networks (ANN), and Support Vector Machines (SVM)—to predict short-term electrical load demand. Each model was trained using historical load data enriched with temporal features to capture daily, seasonal, and other variations in electricity consumption. The SVM model demonstrated strong predictive capability, achieving a Mean Absolute Error (MAE) of 1887.41 MW, Mean Squared Error (MSE) of 6942.36 GW, Root Mean Squared Error (RMSE) of 2634.83 MW, and an R2 score of 91.95%. In comparison, the ANN model showed slightly higher errors, while the LR model had the highest error rates, indicating its limitations in capturing non-linear relationships. The results suggest that SVM and ANN models are more effective than LR for STLF due to their ability to handle non-linear dependencies and high-dimensional data. This study highlights the potential of machine learning techniques in enhancing the accuracy and reliability of load forecasting, ultimately supporting better decision-making in energy management.

STFM: Accurate Spatio-Temporal Fusion Model for Weather Forecasting

Meteorological prediction is crucial for various sectors, including agriculture, navigation, daily life, disaster prevention, and scientific research. However, traditional numerical weather prediction (NWP) models are constrained by their high computational resource requirements, while the accuracy of deep learning models remains suboptimal. In response to these challenges, we propose a novel deep learning-based model, the Spatiotemporal Fusion Model (STFM), designed to enhance the accuracy of meteorological predictions. Our model leverages Fifth-Generation ECMWF Reanalysis (ERA5) data and introduces two key components: a spatiotemporal encoder module and a spatiotemporal fusion module. The spatiotemporal encoder integrates the strengths of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), effectively capturing both spatial and temporal dependencies. Meanwhile, the spatiotemporal fusion module employs a dual attention mechanism, decomposing spatial attention into global static attention and channel dynamic attention. This approach ensures comprehensive extraction of spatial features from meteorological data. The combination of these modules significantly improves prediction performance. Experimental results demonstrate that STFM excels in extracting spatiotemporal features from reanalysis data, yielding predictions that closely align with observed values. In comparative studies, STFM outperformed other models, achieving a 7% improvement in ground and high-altitude temperature predictions, a 5% enhancement in the prediction of the u/v components of 10 m wind speed, and an increase in the accuracy of potential height and relative humidity predictions by 3% and 1%, respectively. This enhanced performance highlights STFM’s potential to advance the accuracy and reliability of meteorological forecasting.

Multi-model transfer function approach tuned by PSO for predicting stock market implied volatility explained by uncertainty indexes

This paper studies the forecasting power of uncertainty emanating from the commodities market, energy market, economic policy, and geopolitical threats to the CBOE Volatility Index (VIX). In this study, the relationship between the various uncertainty metrics throughout the period 2012—2022, using a multi-model transfer function technique optimized by particle swarm optimization (PSO) is estimated. Furthermore, we utilize PSO for parameter optimization within the multi-model framework, improving model performance and convergence speed. According to empirical findings, the CBOE Volatility Index reacts nonlinearly to the uncertainty indices. Specifically, the conclusions of the performance metrics show that the OVX index (MAPE: 4.1559%; RMSE: 1.0476% and W: 96.74%) outperforms the geopolitical risk index, the Bloomberg energy index, and the economic policy uncertainty index in predicting the volatility of the US equities market. Although individual models have generated respectful performance, results from the aggregate simulation show that when all predictors are combined, they simultaneously provide better performance indicators (MAVE: 2.7511 %; RMSE: 0.7361%; R2: 98.93%) than when they are estimated separately. In addition, results provide evidence that, when considering non-linear patterns in the data, the multi-model transfer function technique calibrated using PSO demonstrates its outperformance over autoregressive baseline models, traditional econometric models, and deep learning techniques. The effectiveness and accuracy of the multi-model transfer function method tuned by PSO as a forecasting tool are confirmed by the convergence analysis of the cost function. Our methodology innovates by employing a multi-model transfer function technique, which captures the complex and nonlinear relationships between uncertainty indicators and the VIX more comprehensively than traditional single-model approaches. These results are important for traders in terms of hedging as well as portfolio diversification by investing in defensive equities and for policymakers in terms of reliability and preciseness of volatility forecasts.

Top-up Frequency Forecasting for Lao Telecommunication Company Using Time-Series Analysis

The study aimed to forecast the top-up frequency for Lao Telecommunication Company (LTC) customers using time-series analysis. Accurate forecasts help the company optimize resource allocation, improve customer service, and boost revenue by anticipating customer demand. The time-series analysis techniques are employed, such as autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average + exogenous variables (SARIMAX) and exponential smoothing (ETS) models, to analyze historical top-up data. The data is pre-processed, identified patterns, and trained the model to forecast future top-up frequencies. The model's predictions closely aligned with the actual data, indicating its effectiveness. The ARIMA model produced reliable forecasts for short-term top-up frequencies, showing the best performance overall with the lowest RMSE and MAE, and a positive R-squared indicating some ability to explain the data. SARIMAX and EST both perform poorly, with very high RMSE and MAE, and negative R-squared values. The results demonstrated that time-series analysis could enhance decision-making in the telecommunications industry by enabling better forecasting of customer behavior. This helps in optimizing marketing strategies, inventory management, and service availability.

Enhanced Foreign Exchange Volatility Forecasting Using Ceemdan with Optuna-Optimized Ensemble Deep Learning Model

Foreign Exchange (FX) is the largest financial market in the world, with a daily trading volume that significantly exceeds that of stock and futures markets. The prediction of FX volatility is a critical financial concern that has garnered significant attention from researchers and practitioners due to its far-reaching implications in the financial markets. This paper presents a novel hybrid ensemble forecasting model integrating a decomposition strategy and three deep learning (DL) models: Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Convolutional Neural Network (CNN). This combination addresses individual models' limitations and further improves the accuracy and stability of FX volatility forecasting. The proposed approach utilizes the CEEMDAN technique to decompose volatility into multiple distinct intrinsic mode functions (IMFs) and merges these IMFs with GARCH and EGARCH volatilities to form the input dataset for the DL models. In addition, we employed an attention mechanism to improve the effectiveness of the DL techniques. Furthermore, the hyperparameters for the DL models are optimized using the Optuna algorithm. Finally, a hybrid ensemble model for forecasting exchange rate volatility is developed by combining the predictions of three distinct DL models. The proposed approach is evaluated against various benchmark models using evaluation measures such as MSE, MAE, HMSE, HMAE, RMSE, Q-LIKE, and the model confidence set (MCS) approach. The results demonstrate that our proposed approach provides accurate and reliable forecasts of FX volatility under different forecasting regimes, making it a valuable tool for financial practitioners and researchers.

Medium-term forecast of the dynamics of life attitudes of Russians

The article presents a method for forecasting trends in changes in mass consciousness in terms of life attitudes, i.e. those values according to which people try to build their lives. Modern applied sociology successfully uses the forecast of electoral and consumer behaviour of citizens. The time lag of forecast reliability: for electoral behaviour - 4-5 weeks, for consumer behaviour - up to a year. Forecasting for 10-20 years is practiced mainly in demography. It is more difficult to forecast changes in mass consciousness based on value attitudes of citizens. If we assume that the development of the state is evolutionary, then the combination of heuristic and mathematical methods allows us to forecast trends in the attitudes of the population aged 40 and older, as basic for the economic and political functioning of the state. In methodological terms, the article proposes a procedure for forecasting trends only, without claiming high accuracy of numerical indicators, with mandatory consideration of weak variability of basic numerical indicators measured as of 2024. To forecast trends, there were used data from an all-Russian representative sociological study, accumulated in integral indices, as well as in verbal constructs using factor analysis. The combination of these methods allowed to make a generally stable 20-year forecast of trends in changes in the life attitudes of Russians. As a tool for a detailed forecast indices as integrated indicators of the intensity and direction of a trend were used, and for a generalised forecast - verbal constructs formed on the basis of factor analysis. The choice of the subject of the forecast is based on the hypothesis that the &amp;amp;quot;catalyst&amp;amp;quot; of life attitudes are three components of life activity: the life trajectory chosen by the individual, including the preferred types of life activity; the identification of the individual with reference groups as a contact environment, a mechanism of value communication and self-realisation; the choice of forms of social behaviour by the individual based on the basic moral principles of people's life activity. These three aspects of the population's life activity are not only socially (phylogenetically), but also to a large extent biologically (ontogenetically) determined, therefore they are highly stable, that allows us to talk about the stability of the medium-term forecast of trends in the interval of up to 20 years. Thus, the article proposes the tested in an applied sociological study combined method of medium-term forecast of social groups' attitudes in a situation where repeated studies are not conducted to build a dynamic series of empirical indicators. The forecast presented in the article operates with indicators that are important for consideration in the practice of medium-term planning of the state’s social policy.

Testing the reliability of soil moisture forecast for its use in agriculture

The increased frequency and intensity of drought events are among the major impacts of climate change in various regions worldwide, including Central Europe. These changes have increased the demand for precise drought monitoring and forecasting tools and their validation. The Czech Drought Monitoring System, which is widely utilized across Central Europe, provides daily soil moisture monitoring and medium-range forecasts using the SoilClim model. The main objective of this study was to describe and evaluate the spatiotemporal reliability of these forecasts. The forecasting performance was evaluated for three variables (relative soil moisture content, soil moisture deficit, and drought intensity) and was evaluated using Pearson’s correlation, mean bias error, and mean absolute error. All the statistical analyses were performed on data from the years 2019 to 2021 aggregated at the administrative district level in the Czech Republic. The growing season data were analyzed in detail to assess the forecasting accuracy during spring and summer. Furthermore, the ability to forecast rapid changes in the soil moisture content according to changes in meteorological variables, such as precipitation and air temperature, was evaluated. Our findings demonstrate that the SoilClim model forecasts are accurate and suitable for practical applications in sectors such as agriculture and forestry. The lowest reported correlation between the monitored and forecasted values was +0.68 for nine-day forecasts at a soil depth of 0–40 cm. For shorter forecast periods of one and four days, the correlation values were +0.80 or greater. For drought intensity, the errors did not exceed one category of drought severity. We identified summer as the most dynamic season, with corresponding variations in the soil moisture and meteorological forecasting accuracy. This study validates the ability of the Czech Drought Monitoring System to provide reliable soil moisture forecasts, thus contributing to our ability to manage and mitigate drought impacts effectively.

Semi-supervised prediction method for time series based on Monte Carlo and time fusion feature attention

Accurate and reliable forecasting of time sequences is a challenging task in cyber–physical systems (CPS). Traditional time series prediction models struggle to provide accurate predictions for diverse types of time series data, especially with missing data due to variations in scale, types, and the complexity of real-world production environments. In this paper, we introduce a hybrid model named Temporal Feature Fusion Attention-based Monte Carlo Semi-supervised Long Short Term Memory (LSTM) network to address this issue. The model encodes the current state and historical information using a current time feature state vector. It then calculates the hidden feature vectors for the time series at different time points (past, present, and future), as well as for the current Monte Carlo filtering sequences. This approach leverages the correlation of time series features, transfers and fuses crucial historical features within the sequence with the optimized sequence feature information from the Monte Carlo algorithm. Our experiments confirm that with 10% of labeled data missing, our proposed method significantly improves the evaluation metric Mean Absolute Percentage Error (MAPE) by 17.827% compared to the baseline LSTM model. Moreover, our method surpasses other state-of-the-art methods across four distinct time series datasets, achieving the best prediction results.

Daily and Weekly Geometric Brownian Motion Stock Index Forecasts

In this manuscript, daily and weekly geometric Brownian motion forecasts are obtained and tested for reliability for three indexes, DJIA, NASDAQ and S&amp;P 500. A twenty-year rolling window is used to estimate the drift and diffusion components, and applied to obtain one-period-ahead geometric Brownian motion index values and associated probabilities. Expected values are estimated by totaling up the product of the index value and its associated probabilities, and test for reliability. The results indicate that geometric Brownian-simulated expected index values estimated using one thousand simulations can be reliable forecasts of the actual index values. Expected values estimated using one or ten simulations are not as reliable, while those obtained using at least one hundred simulations could be useful.