Individual Forecasts Research Articles

Experimental Comparison of Two Main Paradigms for Day-Ahead Average Carbon Intensity Forecasting in Power Grids: A Case Study in Australia

Accurate carbon intensity forecasts enable consumers to adjust their electricity use, reducing it during high fossil-fuel generation and increasing it when renewables dominate. Existing methods for carbon intensity forecasting can be categorized into a source-disaggregated approach (SDA), focused on delivering individual generation forecasts for each potential source (e.g., wind, brown-coal, etc.), and a source-aggregated approach (SAA), attempting to produce a single carbon intensity forecast for the entire system. This research aims to conduct a thorough comparison between SDA and SAA for carbon intensity forecasting, investigating the factors that contribute to variations in performance across two distinct real-world generation scenarios. By employing contemporary machine learning time-series forecasting models, and analyzing data from representative locations with varying fuel mixes and renewable penetration levels, this study provides insights into the key factors that differentiate the performance of each approach in a real-world setting. The results indicate the SAA proves to be more advantageous in scenarios involving increased renewable energy generation, with greater proportions and instances when renewable energy generation faces curtailment or atypical/peaking generation is brought online. While the SDA offers better model interpretability and outperforms in scenarios with increased niche energy generation types, in our experiments, it struggles to produce accurate forecasts when renewable outputs approach zero.

SINGULAR SPECTRAL ANALYSIS OF TEMPERATURE MULTIDIMENSIONAL TIME SERIES

In the paper the algorithm of singular structural analysis and forecasting of a multidimensional series by the MSSA method is used. The program was developed, in which the steps of the method for the selection of singular decomposition components were implemented, the analysis and forecast of real time series was carried out. Singular spectrum analysis (SSA) is increasingly used in the study of time series. Unlike other methods of statistical research of time series, this method is used to study the structure, selection of individual components and forecast of both stationary and non-stationary time series. It does not require an analytical model of the series. In fact, this approach is based on the method of principal components. It is based on the transformation of a series into a matrix and its singular decomposition. After identifying the components of the singular decomposition, their grouping takes place, which leads to the decomposition of the original series into additive components, such as trend, oscillations (periodics), and noise. The SSA method allows you to continue the structure of the time series, thus building a forecast (continuation). An important direction of the development of the SSA method as a method of time series analysis is its generalization for the analysis of multidimensional time series. The method is known as MSSA (Multi-Channel SSA) or E-EOFs (Extended Empirical Orthogonal Functions). In this case, the expected result is the simultaneous decomposition of several series into interpreted components. However, a sufficiently complete theory for MSSA does not exist.

Learning to be overprecise

AbstractWe replicate and extend two studies on the dynamics of overconfidence among financial professionals. Using 20 years of data from the ZEW Financial Market Survey with over 40,000 individual forecasts of confidence intervals, we document that participants are overprecise during the entire time period with no evidence of learning on the aggregate. We confirm that professionals update in a Bayesian manner after hits and misses by contracting or expanding their confidence intervals, respectively. However, this updating is insufficient to reach proper calibration. We cannot confirm other predictions of a Bayesian model. An explanation based on self-attribution bias fits the data better.

Incorporating Honey Badger Algorithm in Estimating Gamma Distribution With Application to Stock Price Modelling

This study evaluates the performance of various estimation methods in stock price analysis across diverse parameters, focusing on the Honey Badger Algorithm (HBA). The purpose is to determine the most accurate and reliable method for parameter estimation. Methodologically, we analyze data spanning eight years from publicly traded Malaysian property companies, employing financial metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Our findings highlight HBA’s consistent precision in parameter estimation, with values closely aligning with initial parameters across different stock sizes. For example, HBA-Gamma model achieves an MAE of 0.0592 and an RMSE of 0.8458 for 13 stocks, demonstrating its proficiency in capturing stock price distributions in dynamic markets. In contrast, the Artificial Immune System (AIS) provides reasonable estimates but with higher variability. The Regression Method exhibits mixed outcomes, displaying accuracy in some cases but notable variability and reduced precision, especially with larger datasets. The Moment Method, while adequate, shows slightly higher variance compared to both HBA and AIS. Further analysis using Log Likelihood values confirms HBA’s superior fit to the data, consistently surpassing AIS, Regression Method, and Moment Method in likelihood maximization across various stock numbers. Specifically, HBA exhibits lower MAE and RMSE values of 0.1034 and 0.06723, respectively, for 26 stocks, further validating its effectiveness in parameter estimation and stock price prediction. These findings underscore the importance of integrated approaches that account for market nuances rather than relying solely on individual model forecasts. The results affirm HBA’s potential for informed investment decision-making, emphasizing its robust performance and enhanced predictive capabilities compared to alternative methodologies. However, further research is needed to assess the generalizability of these findings to other markets and contexts.

Entropy-Based Strategies for Multi-Bracket Pools.

Much work in the parimutuel betting literature has discussed estimating event outcome probabilities or developing optimal wagering strategies, particularly for horse race betting. Some betting pools, however, involve betting not just on a single event, but on a tuple of events. For example, pick six betting in horse racing, March Madness bracket challenges, and predicting a randomly drawn bitstring each involve making a series of individual forecasts. Although traditional optimal wagering strategies work well when the size of the tuple is very small (e.g., betting on the winner of a horse race), they are intractable for more general betting pools in higher dimensions (e.g., March Madness bracket challenges). Hence we pose the multi-brackets problem: supposing we wish to predict a tuple of events and that we know the true probabilities of each potential outcome of each event, what is the best way to tractably generate a set of n predicted tuples? The most general version of this problem is extremely difficult, so we begin with a simpler setting. In particular, we generate n independent predicted tuples according to a distribution having optimal entropy. This entropy-based approach is tractable, scalable, and performs well.

The Impact of Oil Prices on the GDP of V4 Countries

This study is concerned with forecasts of GDP growth for the Visegrad countries (V4-hereafter) and oil prices simultaneously. The series for GDP and Oil prices are quarterly, covering the period from December 1, 2000, to October 1, 2023. Neural network techniques were performed to generate individual forecasts. The forecasts for oil prices maintain higher accuracy than the GDP ones due to autoregressive lags. In other words, current oil prices absorb significant influence from past prices. The GDP forecasts for the V4 group indicate tremendous shock and moving growth to a lower steady state. The lower growth observed by the end of the period under review can be attributed to two subsequent non-economic shocks: the Russia-Ukraine war and the COVID-19 pandemic. The V4 countries faced difficulties after the outbreak of COVID-19, mainly due to the stringency measures. In addition to accelerating inflation, the war also disrupted energy commodities within supply chains. These and other facts justify the low growth that awaits these countries until 2028. Ultimately, the oil price until 2028 is estimated to oscillate between $60 and $90 per barrel.

Carbon price forecasting using leaky integrator echo state networks with the framework of decomposition-reconstruction-integration

Carbon trading is one of the strategies to achieve the goal of dual carbon. However, carbon prices exhibit non-stationary and nonlinear characteristics, posing significant challenges for accurate forecasting. Inspired by the ideas of “division and conquest” and “granularity reconstruction”, a decomposition-reconstruction-integration framework is built for carbon price forecasting. The proposed model leverages a comprehensive ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and sample entropy (SE) to extract features from two perspectives of time and complexity, and thus the one-dimensional time series of carbon price is reconstructed into different items. Subsequently, leaky integrator echo state networks (LiESNs) are utilized to forecast each of these items individually. These individual forecasts are then integrated to generate the final point forecast. To evaluate the uncertainty of the point forecast, a Gaussian process model is applied to interval forecast. The proposed model comprehensively considers the time series features of carbon price including the temporal modal features in the decomposition stage, the entropy probability features in the reconstruction stage, and the essential temporal dependencies via dynamic reservoir of interconnected neurons in the integration forecasting stage. Experimental results demonstrate the model's exceptional forecasting performance, surpassing the effectiveness of alternative approaches.

Grand ensemble forecasts verification based on two high resolution (∼12 km) global ensemble prediction systems

The primary objective of this study is to quantitatively assess the skill of grand-ensemble forecasts generated from two high-resolution global ensemble prediction systems (EPSs). We have used the National Centre for Medium Range Weather Forecasting (NCMRWF), India global EPS, denoted as NEPS, and India Meteorological Department (IMD) EPS, known as GEFS. Both of these operational EPSs operate at ∼12 km horizontal grid size and produce ensemble forecast outputs. In this study, these two high-resolution EPSs based grand-ensemble products have been evaluated.Statistical analysis is conducted using seven-day forecasts from NEPS's 22 perturbed members and GEFS's 20 perturbed members. The performance of the global grand ensemble, consisting of 42 members, is evaluated for summer monsoon season, 1st July to 30th September 2019 in comparison to the individual EPS forecasts. The verification metrics are prepared using variables such as Zonal Wind and Temperature at 850 hPa, Geopotential at 500 hPa and Precipitation. These metrics are applied across both the Northern Hemisphere (20°N-90°N) and over the domain located in the South Asia encompassing India and its neighbouring areas (0–40 oN & 50–100°E). The verification metrics employed include ensemble spread, root-mean-square error (RMSE) of ensemble mean, Brier score, relative operating characteristic, area skill score, reliability, rank histogram, ranked probability score, continuous ranked probability score, and relative economic value.We noted that the grand ensemble forecasts in this study carries a higher skill compared to the two constituent models for lead time >3 days and for higher forecast probability for the longer lead times. Lower RMSE in the grand ensemble for longer lead-time forecasts (day 4–7) indicates a better representation when compared to forecasts from individual EPSs. However, it is worth noting that shorter lead-time forecasts (day 1–3) in the grand ensemble could potentially result in over-dispersion. Brier scores further indicate an enhancement in predictability of nearly one day in grand ensemble compared to the individual EPSs for T850, U850 and Z500. RPS of rainfall in a smaller domain centring on India indicates a better score for grand ensemble compared to both the EPS. Grand ensembles demonstrate its ability to provide better reliability and discrimination quality. The economic value of the forecasts based on grand ensemble, is higher with a wider range of cost/loss ratio compared to those in the individual EPSs.

Dynamic model of the functional parameter degradation changes in radio-electronic systems with two non-stationary random components corresponding to two types of noise

Problem statement. The processes of aging and degradation caused by the influence of environmental conditions, leading to changes in the functional parameters of radio-electronic systems, negatively affect the output characteristics and reduce the period of operation. The currently existing model of changes in radio-electronic system parameters, used for individual forecasting by extrapolation, includes only one random component representing various types of noise. At the same time, practical studies shows that the maximum amplitudes of different types of noise change at different rates. Therefore, for a more complete representation of the process of electromagnetic parameter changes over time, it is necessary to develop a model that takes into account this effect. The article discusses this problem using the example of the linear stabilizer output voltage. Goal. The main goal of the research presented in this article is to develop a model of functional parameter degradation that takes into account changes in maximum amplitudes of various types of noise in order to increase the accuracy of individual forecasting and the potential period of the device operation. Results. A generalized model of the electromagnetic parameter changes of the radio-electronic systems with two nonstationary random components characterizing various types of noise is presented. A software implementation of the presented model successfully verified on experimental data. Practical significance. The extended model of the functional parameter changes in radio-electronic systems can be used both in individual forecasting by extrapolation, and in the development of systems with a long period of operation, as well as devices intended for autonomous use. The prospects of using the presented model to simulate changes in functional parameters and determine the possibilities of compensating these changes are shown on the example of a frequency synthesizer with an automatic output power control system.

Forecast combination in agricultural economics: Past, present, and the future

AbstractForecasts are common in agricultural settings where they are routinely used for decision‐making. The advent of the computer age has allowed for rapid generation of individual forecasts that can be updated in real time. It is well known that the selection and use of a single forecast can expose the forecaster to serious error as a result of model mis‐specification. Forecast combination avoids this problem by combining information from different forecasts. Although forecast combination can be as simple as averaging across forecasts, advances in machine learning have made it possible to combine forecasts according to more complicated weighting schemes and criteria. We provide an overview of forecast combination techniques, including those at the frontier of current practice and involving machine learning. We also provide a retrospective on the use of forecast combination in agricultural economics and prospects for the future. Several of the techniques are illustrated in an application to forecasting nationwide corn and soybean planted acreage and we demonstrate how forecast combination can improve expert USDA projections.

AI‐Based Ensemble Flood Forecasts and Its Implementation in Multi‐Objective Robust Optimization Operation for Reservoir Flood Control

AbstractInforming reservoirs with forecasts is highly important for real‐time flood control. This study proposed a forecast‐informed methodology framework for reservoir flood control operation under uncertainty. A new combination of two post‐processing methods, that is, the Cloud model and error‐based copula functions, were developed to merge individual AI‐based forecasts to ensemble flood forecasts, so called stochastic errors‐based Cloud (SE‐Cloud). A multi‐objective robust optimization model (MRO) integrating the risk, resilience, and vulnerability was then proposed to tackle flood control problems under ensemble forecasts; for comparison, a two‐objective stochastic optimization model (TSO) was developed to minimize the expected highest reservoir level and peak release. The proposed methodology was applied to the Lishimen reservoir in the Shifeng River subbasin, China, aiming to comprehensively verify the relationships among deterministic forecasts, ensemble forecasts, and flood control performance. Results showed that the Cloud model could effectively integrate different models and improve forecast accuracy. But a higher deterministic forecast quality did not consistently result in improved flood control performance. SE‐Cloud could capture the peak flow and effectively characterize forecast uncertainties and increased hypervolume values by 13.14%–39.65% compared to the Cloud model, indicating the superiority of ensemble forecasts in generating robust solutions over individual deterministic forecasts. MRO released more inflow than TSO, decreasing the expected highest water level by 0.05 m and incrementing the expected peak release by 4.29%. However, with downstream resilience value remaining at zero, it is demonstrated that MRO improving upstream vulnerability did not necessarily diminish resilience. The enhanced robustness highlights the potential of AI‐based ensemble forecasts in flood control.

Predicting the Equity Premium with Combination Forecasts: A Reappraisal

Abstract This paper reappraises the usefulness of combining individual forecasts for predicting the U.S. equity premium. For comparison, we also consider penalized regression and dimension reduction approaches. We fail to find evidence of predictive ability in recent decades, regardless of the forecasting method used. Further analysis shows that an increase in the correlation of individual forecast errors is an important factor in the declining performance of combination forecasts.()

Study of the Accuracy Characteristics of the Mathematical Model for Predicting Changes in the Error of the Working Measurement Standard

The accuracy of the forecasting method and its prior estimation when predicting changes in the error of the working measurement standard are one of the key issues. In the course of studying these issues, a number of components of the forecasting error were assessed, and various methods and mathematical models of forecasting were compared. A comparative analysis showed that the studied mathematical model for individual forecasting of changes in the error of a working standard available at «Mathematical model for predicting changes in the value of the critical component of the error of the working measurement standard of the unit of magnitude taking into account prior information» has higher accuracy in comparison with the considered known forecasting methods. The estimates of the parameters of the forecasting function obtained with the application of the model under consideration available at «Determination of parameters for metrological maintenance of measuring instruments by the technical and economic criterion», using the expressions of the transition to forecasting the probability of metrological serviceability of the working measurement standard, specific costs for metrological maintenance, and damage from the use of the working measurement standard in the state of metrological failure, allow to substantially increase the validity of decisions to refine the value of the interval between the certification of the working standard, which is initially established (within the framework of the ITCM).

Individual household load forecasting using bi-directional LSTM network with time-based embedding

Accurate individual household load forecasting is essential for effectively managing energy demand and promoting efficient energy consumption. This study evaluates the performance of various deep learning models for individual household load forecasting, specifically using the Smart Grid Smart City (SGSC) dataset. Feature engineering is conducted, producing two distinct sets of features, and the models’ accuracy in predicting individual household loads is assessed using both basic and derived feature sets. The results demonstrate that the T2VBiLSTM model outperforms other models, achieving an average mean absolute percentage error (MAPE) of 74.90% and a root mean square error (RMSE) of 0.433 kW. The incorporation of a wider range of features, including maximum load, minimum load, and load range over time, is emphasized to enhance forecasting accuracy. These features capture long-term load behavior, enabling the models to comprehend complex energy consumption patterns and generate more accurate and reliable predictions. Moreover, limitations in using MAPE as a loss function for load forecasting at the individual customer level are revealed, due to its sensitivity to scale, asymmetry, outliers, and uniform weighting assumption. Alternative loss functions like mean absolute error (MAE) are recommended, as they treat all errors equally and better capture data peaks. While this study focuses on the SGSC dataset, the findings have broader implications for efficiently managing energy demand and promoting energy consumption on a larger scale.

Flexible global forecast combinations

Forecast combination – the aggregation of individual forecasts from multiple experts or models – is a proven approach to economic forecasting. To date, research on economic forecasting has concentrated on local combination methods, which handle separate but related forecasting tasks in isolation. Yet, it has been known for over two decades in the machine learning community that global methods, which exploit task-relatedness, can improve on local methods that ignore it. Motivated by the possibility for improvement, this paper introduces a framework for globally combining forecasts while being flexible to the level of task-relatedness. Through our framework, we develop global versions of several existing forecast combinations. To evaluate the efficacy of these new global forecast combinations, we conduct extensive comparisons using synthetic and real data. Our real data comparisons, which involve forecasts of core economic indicators in the Eurozone, provide empirical evidence that the accuracy of global combinations of economic forecasts can surpass local combinations.

Do indirect measures of attitudes improve our predictions of behavior? Evaluating and explaining the predictive validity of GATA

The generalization of the results accumulated to date has shown that the implicit measures of attitudes (some even suggest defining them with a less pretentious term “indirect”) show a disappointingly weak predictive potential in relation to real behavior. Thus, the predictive validity of the Graphical Association Test of Attitude (GATA), which also claims to be an indirect method, has been questioned. To check this assumption, we analyzed the results obtained with GATA in 64 predictions provided that the predicted outcome could be verified by real action. Such forecasts cover the domains of electoral, consumer and communicative behavior. In some cases, the prediction based on the data from a representative sample was checked referring to the actual behavior of the group represented by the sample, e.g., the electorate, or the consumers of a certain category of goods, etc. In other cases, the accuracy of the forecast was checked for each respondent. This allows to avoid the effect of “mutual compensation” of erroneous forecasts with opposite valence. The test method consisted of a comparison of the prediction accuracy of pairs of “control” and “experimental” prediction models: the only difference identified was that the latter used the data from indirect measurements of GATA as an additional factor of action. In the article, all models are presented in their simplest and most transparent versions. The results of the conducted meta-analysis do not fully correspond to the general trend: the use of the GATA data significantly and continuously improves the accuracy of predicting behavior. In addition, the incremental effect on the accuracy of individual forecasts (for each respondent) turned out to be higher than that of the sample-based group forecasts.

The performance of Climate Information Service in delivering scientific, local, and hybrid weather forecasts: A study case in Bangladesh

Access to reliable and skillful Climate Information Service (CIS) is crucial for smallholder farmers in Bangladesh to mitigate the impacts of rainfall variability and extremes. This study aims to systematically evaluate the performance of CIS in providing Scientific Forecast (SF) and Local Forecast (LF) to smallholders in Bangladesh. The results were then compared with farmers’ perceptions of the forecast accuracy. Additionally, the skill of a simple hybrid forecast (HF), which is an integrated system of SF and LF, was assessed using the ERA5 and ground observation datasets as benchmarks. The SF and LF data were obtained from the meteoblue hindcast and from the interview, respectively. The results indicate that, overall, LF exhibits slightly higher skill compared to SF when evaluated against the ERA5 dataset. The forecast performance, however, declines by almost half when the ground-based observations are used, associated with high false alarms. Farmers, on the other hand, perceived SF to possess superior performance compared to LF. This study demonstrates that combining the SF and LF into a simple HF yields higher forecast skill than either individual forecast, highlighting the importance of HF to deliver a reliable and trustworthy weather forecast.

WoFS and the Wisdom of the Crowd: The Impact of the Warn-on-Forecast System on Hourly Forecasts during the 2021 NOAA Hazardous Weather Testbed Spring Forecasting Experiment

Abstract During the 2021 Spring Forecasting Experiment (SFE), the usefulness of the experimental Warn-on-Forecast System (WoFS) ensemble guidance was tested with the issuance of short-term probabilistic hazard forecasts. One group of participants used the WoFS guidance, while another group did not. Individual forecasts issued by two NWS participants in each group were evaluated alongside a consensus forecast from the remaining participants. Participant forecasts of tornadoes, hail, and wind at lead times of ∼2–3 h and valid at 2200–2300, 2300–0000, and 0000–0100 UTC were evaluated subjectively during the SFE by participants the day after issuance, and objectively after the SFE concluded. These forecasts exist between the watch and the warning time frame, where WoFS is anticipated to be particularly impactful. The hourly probabilistic forecasts were skillful according to objective metrics like the fractions skill score. While the tornado forecasts were more reliable than the other hazards, there was no clear indication of any one hazard scoring highest across all metrics. WoFS availability improved the hourly probabilistic forecasts as measured by the subjective ratings and several objective metrics, including increased POD and decreased FAR at high probability thresholds. Generally, expert forecasts performed better than consensus forecasts, though expert forecasts overforecasted. Finally, this work explored the appropriate construction of practically perfect fields used during subjective verification, which participants frequently found to be too small and precise. Using a Gaussian smoother with σ = 70 km is recommended to create hourly practically perfect fields in future experiments. Significance Statement This work explores the impact of cutting-edge numerical weather prediction ensemble guidance (the Warn-on-Forecast System) on severe thunderstorm hazard outlooks at watch-to-warning time scales, typically between 1 and 6 h of lead time. Real-time forecast products in this time frame are currently provided on an as-needed basis, and the transition to continuous probabilistic forecast products across scales requires targeted research. Results showed that hourly probabilistic participant forecasts were skillful subjectively and statistically, and that the experimental guidance improved the forecasts. These results are promising for the implementation and value of the Warn-on-Forecast System to provide improved hazard timing and location guidance within severe weather watches. Suggestions are made to aid future subjective evaluations of watch-to-warning-scale probabilistic forecasts.

Integrated forecasting method of medium-and long-term runoff by ridge regression based on optimal sub-model selection

Abstract Numerous studies have demonstrated that the combination models can improve the runoff forecast performance compared to individual forecasts. However, some models do not take into account the effects of inappropriate sub-models on the combination models. Based on this, a medium-and long-term runoff integrated forecasting method based on optimal sub-models selection was proposed. First, the sub-models, including linear regression (MLR), BP neural network (BPNN), wavelet neural network (WNN), and support vector regression (SVR), are optimally selected based on the nearness degree. Second, ridge regression (RR) is used to combine the optimal sub-models to predict runoff. Finally, the Guandi hydropower station is taken as an example to verify the effect of the integrated forecasting model. The results show that SVR, BPNN, and WNN are the optimal sub-models, and RR-3 is the optimal integrated forecasting model composed of the optimal sub-models. In addition, compared with the other two combination models, the RR-3 performs better.

Complex Real-Time Monitoring and Decision-Making Assistance System Based on Hybrid Forecasting Module and Social Network Analysis

Timely short-term spatial air quality forecasting is essential for monitoring and prevention in urban agglomerations, providing a new perspective on joint air pollution prevention. However, a single model on air pollution forecasting or spatial correlation analysis is insufficient to meet the strong demand. Thus, this paper proposed a complex real-time monitoring and decision-making assistance system, using a hybrid forecasting module and social network analysis. Firstly, before an accurate forecasting module was constructed, text sentiment analysis and a strategy based on multiple feature selection methods and result fusion were introduced to data preprocessing. Subsequently, CNN-D-LSTM was proposed to improve the feature capture ability to make forecasting more accurate. Then, social network analysis was utilized to explore the spatial transporting characteristics, which could provide solutions to joint prevention and control in urban agglomerations. For experiment simulation, two comparative experiments were constructed for individual models and city cluster forecasting, in which the mean absolute error decreases to 7.8692 and the Pearson correlation coefficient is 0.9816. For overall spatial cluster forecasting, related experiments demonstrated that with appropriate cluster division, the Pearson correlation coefficient could be improved to nearly 0.99.