Forecasting Strategies Research Articles

The Capacity and Robustness Trade-Off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting

The Capacity and Robustness Trade-Off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting

Assessing the Profit Impact of ARIMA and Neural Network Demand Forecasts in Retail Inventory Replenishment

This study investigates the integration of demand forecasting and inventory replenishment strategies to enhance retail profitability. A deterministic optimal replenishment model is utilized to analyze the predictive performance of various neural network architectures and ARIMA models using real sales data. The predictive accuracy and subsequent influence on optimal firm profits over a multi-period planning horizon is assessed. The Integer Programming model devised optimizes daily replenishment across multiple retail routes, taking into account sales revenue, supply costs, inventory holding, sales loss, and transportation expenses. The study is distinctive in its dual assessment: it evaluates both the accuracy of forecasting methods and their direct impact on profitability through systematic inventory decisions. Neural network architectures selected for minimizing error in product sales predictions have 6% lower mean squared error compared to Akaike Information Criterion minimizing ARIMA models. For longer horizon predictions necessary in performance gap grows larger, e.g., with %60 difference for predictions 15 days ahead. Predictions reflect as 1.6% higher profits on average, when neural network predictions and more efficient longer planning horizons of the optimization model are preferred. Planning 30 days ahead, optimizing with neural network predictions elicits 2.3% higher profits compared to profits attainable based on ARIMA predictions. Our findings illustrate how different forecasting methods can affect firm profitability by shaping inventory replenishment strategies. By merging mathematical optimization with time series forecasting, this research provides a comprehensive evaluation of how advanced predictive technologies can enhance retail inventory practices and improve profitability.

Multisource Data-Driven Carbon Price Composite Forecasting Model: Based on Feature Selection and Multiscale Prediction Strategy

Accurate prediction of carbon trading prices is crucial for guiding investors to make informed decisions and assisting governments in formulating scientific carbon trading policies. This paper introduces a multisource data-driven carbon price composite forecasting model, aimed at enhancing prediction accuracy through advanced data processing and analysis methods. The model initially employs a secondary decomposition strategy, including Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD) methods, to decompose the original data series into three sub-sequences of different frequencies. Subsequently, it utilizes the Partial Autocorrelation Function (PACF) and Random Forest algorithm for feature selection to determine the input variables for different frequency sequences and conducts in-depth analysis and selection of influencing factors, including unstructured data. Furthermore, the model employs a multiscale forecasting strategy, combining Particle Swarm Optimization (PSO) enhanced Bidirectional Long Short-Term Memory (BiLSTM) and Extreme Gradient Boosting (XGBoost) models, along with the Autoregressive Integrated Moving Average (ARIMA) method, to predict based on the characteristics of different frequency components. Finally, the forecasts are integrated using PSO-BiLSTM to form a comprehensive forecast. Notably, given the high correlation between the trend series and influencing factor variables, the model jointly predicts them. A case study based on the Guangdong carbon market in China demonstrates that the proposed composite forecasting model outperforms other benchmark models, with Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) values of 0.4009, 0.2699, and 0.5183%, respectively. This forecasting model provides an effective tool for predicting and analyzing carbon price fluctuations, offering new insights for precise carbon market price predictions.

Multi-scale drought variability over West Africa and the associated large-scale circulation patterns

This study addresses the critical research gap in understanding regional drought dynamics in West Africa (WA) using advanced multivariate wavelet coherence (MWC) techniques, revealing critical insights into the complex interplay between large-scale climate indices and regional drought patterns. Utilizing the Standardized Precipitation Evapotranspiration Index (SPEI) and self-calibrating Palmer Drought Severity Index (scPDSI), we examined drought characteristics across WA from 1950 to 2018. Our analysis revealed significant drying trends across all seasons, with the Arid zone experiencing the most severe and prolonged drought (158 months) from August 1981 to September 1994, affecting 79.77% of the area. The entire WA region faced its most extensive drought during this period, impacting over 60% of the study area. MWC analysis demonstrated enhanced drought predictability when combining climate indices, particularly the Southern Oscillation Index with the Atlantic Multidecadal Oscillation, yielding high Average Wavelet Coherence values of 0.52 (SPEI) and 0.53 (scPDSI), with substantial Percentage Area of Significant Coherence of 40.19% and 43.68%, respectively. These findings highlight the crucial role of oceanic-atmospheric interactions in modulating drought conditions across WA. Our results provide valuable insights for improving drought forecasting and management strategies in the region, emphasizing the need for integrated approaches that consider multiple climate indices and their combined effects on drought dynamics.

Forecasting severe respiratory disease hospitalizations using machine learning algorithms

BackgroundForecasting models predicting trends in hospitalization rates have the potential to inform hospital management during seasonal epidemics of respiratory diseases and the associated surges caused by acute hospital admissions. Hospital bed requirements for elective surgery could be better planned if it were possible to foresee upcoming peaks in severe respiratory illness admissions. Forecasting models can also guide the use of intervention strategies to decrease the spread of respiratory pathogens and thus prevent local health system overload. In this study, we explore the capability of forecasting models to predict the number of hospital admissions in Auckland, New Zealand, within a three-week time horizon. Furthermore, we evaluate probabilistic forecasts and the impact on model performance when integrating laboratory data describing the circulation of respiratory viruses.MethodsThe dataset used for this exploration results from active hospital surveillance, in which the World Health Organization Severe Acute Respiratory Infection (SARI) case definition was consistently used. This research nurse-led surveillance has been implemented in two public hospitals in Auckland and provides a systematic laboratory testing of SARI patients for nine respiratory viruses, including influenza, respiratory syncytial virus, and rhinovirus. The forecasting strategies used comprise automatic machine learning, one of the most recent generative pre-trained transformers, and established artificial neural network algorithms capable of univariate and multivariate forecasting.ResultsWe found that machine learning models compute more accurate forecasts in comparison to naïve seasonal models. Furthermore, we analyzed the impact of reducing the temporal resolution of forecasts, which decreased the model error of point forecasts and made probabilistic forecasting more reliable. An additional analysis that used the laboratory data revealed strong season-to-season variations in the incidence of respiratory viruses and how this correlates with total hospitalization cases. These variations could explain why it was not possible to improve forecasts by integrating this data.ConclusionsActive SARI surveillance and consistent data collection over time enable these data to be used to predict hospital bed utilization. These findings show the potential of machine learning as support for informing systems for proactive hospital management.

Inventory Management Practices and Their Impact on Operational Efficiency: A Case Study of MGSSK Sugars Ltd.

In today’s highly competitive business environment, effective inventory management is vital for maintaining operational efficiency and ensuring long-term sustainability. Proper inventory control reduces excess stock and prevents stock outs, leading to cost reduction and improved cash flow. By utilizing precise forecasting and strategic replenishment methods, companies can enhance their adaptability to market changes and sustain a competitive advantage. This research provides a thorough examination of inventory management metrics spanning five years. The primary objective of the study is to evaluate the effectiveness of inventory management. The data was primarily collected from secondary sources, including five years of company inventory records. To analyze the data, the ratio analysis method was applied. The results demonstrate notable improvements in the company’s operational efficiency. Key performance indicators include a steady rise in the inventory turnover ratio and a reduction in average stock levels, showcasing effective inventory control aligned with increasing demand and minimizing surplus stock. Moreover, the study reveals a decreasing trend in inventory as a proportion of current assets, indicating a strategic shift away from relying on inventory for revenue generation. Additionally, variations in raw material values, coupled with consistent material consumption, suggest fluctuations in raw material usage efficiency. Furthermore, the relationship between the raw material turnover ratio and inventory holding period highlights the company’s proficiency in optimizing inventory management. The declining inventory conversion period indicates successful transformation of inventory into sales, boosting cash flow and operational efficiency. This improvement is attributed to advanced forecasting and stock replenishment strategies.

Regional Load Forecasting Scheme for Security Outsourcing Computation

Smart grids generate an immense volume of load data. When analyzed using intelligent technologies, these data can significantly improve power load management, optimize energy distribution, and support green energy conservation and emissions reduction goals. However, in the process of data utilization, a pertinent issue arises regarding potential privacy leakage concerning both regional and individual user power load data. This paper addresses the scenario of outsourcing computational tasks for regional power load forecasting in smart grids, proposing a regional-level load forecasting solution based on secure outsourcing computation. Initially, the scheme designs a secure outsourcing training protocol to carry out model training tasks while ensuring data security. This protocol guarantees that sensitive information, including but not limited to individual power consumption data, remains comprehensively safeguarded throughout the entirety of the training process, effectively mitigating any potential risks of privacy infringements. Subsequently, a secure outsourcing online prediction protocol is devised, enabling efficient execution of prediction tasks while safeguarding data privacy. This protocol ensures that predictions can be made without compromising the privacy of individual or regional power load data. Ultimately, experimental analysis demonstrates that the proposed scheme meets the requirements of privacy, accuracy, and timeliness for outsourcing computational tasks of load forecasting in smart grids.

Leveraging the Empirical Wavelet Transform in Combination with Convolutional LSTM Neural Networks to Enhance the Accuracy of Polar Motion Prediction

High-precision polar motion prediction is of great significance for deep space exploration and satellite navigation. Polar motion is affected by a variety of excitation factors, and nonlinear prediction methods are more suitable for polar motion prediction. In order to explore the effect of deep learning in polar motion prediction. This paper proposes a combined model based on empirical wavelet transform (EWT), Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM). By training and forecasting EOP 20C04 data, the effectiveness of the algorithm is verified, and the performance of two forecasting strategies in deep learning for polar motion prediction is explored. The results indicate that recursive multi-step prediction performs better than direct multi-step prediction for short-term forecasts within 15 days, while direct multi-step prediction is more suitable for medium and long-term forecasts. In the 365 days forecast, the mean absolute error of EWT-CNN-LSTM in the X direction and Y direction is 18.25 mas and 15.78 mas, respectively, which is 23.5% and 16.2% higher than the accuracy of Bulletin A. The results show that the algorithm has a good effect in medium and long term polar motion prediction.

Robustness Investigation of ML Models for Stock Price Prediction During Market Volatility: A Case Study of the Japanese Stock Market Amidst the COVID-19 Pandemic

In the evolving realm of financial markets, precise stock price prediction remains a pivotal challenge. This paper investigates the efficacy of traditional machine learning models for stock price forecasting, particularly during the high volatility phase induced by the COVID-19 pandemic, using the Japanese stock market as a case study. This study utilized a dataset from the JPX Tokyo Stock Exchange Prediction competition, selecting two stocks at random to avoid bias. After standardizing the data for consistency, this study employed linear regression and decision trees to project stock prices, comparing their performance during stable periods and the economic upheaval of the pandemic. The analysis revealed that both models' predictive capabilities were compromised during the pandemic, with the decision tree model particularly underperforming. In normal times, linear regression showed improved accuracy for stock 1301 in 2021, while for stock 6758, the model's performance declined. These findings underscore the necessity for robust models that can withstand market turbulences like those observed during the COVID-19 crisis. The research contributes to both academic discussions on financial forecasting and practical strategies for market participants navigating complex stock environments. Future work will focus on developing more resilient forecasting methods to address these challenges.

Agent-Based Modeling for Sustainable Urban Passenger Vehicle Mobility: A Case of Tehran

In response to escalating congestion and deteriorating air quality in urban centers worldwide, exacerbated by overburdened transportation systems, there is an urgent need for accurate traffic forecasting and effective sustainable urban development strategies. This study employs agent-based modeling through four distinctive scenarios for Tehran, I. R. Iran. A synthetic population is meticulously crafted using simulated annealing, enabling the emulation of daily commuting patterns. Results show that by bolstering cycling infrastructure and enhancing public transportation services, reliance on private cars is reduced up to 46%. The introduction of flexible working hours reduces the traffic volumes during peak traffic hours by 47% and significantly altering the daily distances traveled by personal cars, as evidenced by a 1:6 ratio in car volume increase between scenarios emphasizing flexible working hours and those with more conventional traffic patterns. The results provide powerful insights for decisionmakers to manage the traffic especially in high polluted air conditions.

Energy cost forecasting and financial strategy optimization in smart grids via ensemble algorithm

Energy cost forecasting and financial strategy optimization in smart grids via ensemble algorithm

Data fusion integrated network forecasting scheme classifier (DFI-NFSC) via multi-layer perceptron decomposition architecture

The Massive Access Problem of the Internet of Things stands for the access problem of the wireless devices to the Gateway when the device population in the coverage area is excessive. We develop a hybrid model called Data Fusion Integrated Network Forecasting Scheme Classifier (DFI-NFSC) using a Multi-Layer Perceptron (MLP) Decomposition architecture specifically designed to address the Massive Access Problem. We utilize our custom error metric to display throughput and energy consumption results. These results are obtained by emulating the Joint Forecasting–Scheduling (JFS) system on a single IoT Gateway and distinguishing between ARIMA, LSTM, and MLP forecasters of the JFS system. The outcomes indicate that the DFI-NFCS method plays a notable role in improving performance and mitigating challenges arising from the dynamic fluctuations in the diversity of device types within an IoT gateway’s coverage zone.

Can we trust explainable artificial intelligence in wind power forecasting?

Advanced artificial intelligence (AI) models typically achieve high accuracy in wind power forecasting, but their internal mechanisms lack interpretability, which undermines user confidence in forecast value and strategy execution. To this end, this paper aims to investigate the interpretability of AI models, which is crucial but usually overlooked in wind power forecasting. Specifically, four model-agnostic explainable artificial intelligence (XAI) techniques (i.e., Shapley additive explanations, permutation feature importance, partial dependence plot, and local interpretable model-agnostic explanations) are tailored to provide global and instance interpretability for AI models in wind power forecasting. Then, several metrics are proposed to evaluate the trustworthiness of interpretations provided by XAI techniques. Simulation results demonstrate that the proposed XAI techniques can not only identify important features from wind power datasets, but also enable the understanding of the contribution of each feature to the forecast power output for a specific sample. Furthermore, the proposed evaluation metrics aid users in comprehensively assessing the trustworthiness of XAI techniques in wind power forecasting, enabling them to judiciously select suitable XAI techniques for their AI models.

A novel integrated method for improving the forecasting accuracy of crude oil: ESMD-CFastICA-BiLSTM-Attention

The volatility of crude oil price can significantly impact the stability of the crude oil market and even the global economy. Effectively predicting global crude oil price and volatility provides a scientific basis for decision-making for market regulators and investors worldwide, promoting the sound development of the global economy. In this study, we integrate signal processing with deep learning methods to present an optimal forecasting strategy for global crude oil price and volatility. We select the daily price of the WTI crude oil market from April 4, 1983, to December 12, 2023, for calculating volatility. Subsequently, employing extreme-point symmetric empirical mode decomposition (ESMD), K-means clustering, and fast independent component analysis method, we decompose and reconstruct the forecasting data, obtaining independent components with non-Gaussian characteristics. These components serve as inputs to estimate the accuracy of various models, including BiLSTM, Attention, LSTM, SVR, RF, and their combinations, in predicting crude oil price and volatility from both a point prediction and interval prediction perspective. Empirical results demonstrate that data decomposition, reconstruction, and the BiLSTM-Attention model outperform other models in predicting crude oil price and volatility.

Combining evolutionary computation with machine learning technique for improved short-term prediction of UT1-UTC and length-of-day

Over the years, prediction techniques for the highly variable angular velocity of the Earth represented by Earth's rotation (UT1-UTC) and length-of-day (LOD) have been continuously improved. This is because many applications like navigation, astronomy, space exploration, climate studies, timekeeping, disaster monitoring, and geodynamic studies, all rely on predictions of these Earth rotation parameters. They provide early warning of changes in the Earth's rotation, allowing various industries and scientific fields to operate more precisely and efficiently. Thus, in our study, we focused on short-term prediction for UT1-UTC (dUT1) and LOD. Our prediction approach is to combine machine learning (ML) technique with efficient evolutionary computation (EC) algorithms to achieve reliable and improved predictions. Gaussian process regression (GPR) is used as the ML technique with genetic algorithm (GA) as the EC algorithm. GA is used for hyperparameter optimization of GPR model as selecting appropriate values for hyperparameter are essential to ensure that the prediction model can accurately capture the underlying patterns in the data. We conducted some experiments with our prediction approach to thoroughly test its capabilities. Moreover, two forecasting strategies were used to assess the performance in both hindcast and operational settings. In most of the experiments, the data used are the multi-technique combinations (C04) generated by International Earth Rotation and Reference Systems Service (IERS). In one of the experiments, we also investigated the performance of our prediction model on dUT1 and LOD from four different products obtained from IERS EOP 20 C04, DTRF20, JTRF20 and USNO. The prediction products are evaluated with real estimates of the EOP product with which the model is trained. The combined excitations of the atmosphere, oceans, hydrology, and sea level (AAM + OAM + HAM + SLAM) are used as predictors because they are highly correlated to the input data. The results depict the highest performance of 0.412 ms in dUT1 and 0.092 ms/day in LOD, on day 10 of predictions. It is worth noting that the later predictions were obtained by incorporating the uncertainty of the input data as weights in the prediction model, which was a novel approach tested in this study.Graphical

MFTM-Informer: A multi-step prediction model based on multivariate fuzzy trend matching and Informer

Multi-step forecasting is a critical process in various fields, such as disaster warning and financial analysis. Nevertheless, achieving precise multi-step forecasting is challenging due to the intricate nature of the factors influencing the time series, most of which are highly nonlinear and nonstationary. In this paper, a multi-step forecasting model named MFTM-Informer, employing a multiple input multiple output strategy for multivariate trend matching is proposed. The dependent variable and the influencing factors are initially decomposed using multivariate variational modal decomposition to minimize noise. Afterwards, the decomposed data are reconstructed into multivariate trends and fluctuations using sample entropy, enabling the development of tailored forecasting strategies based on data characteristics. A multivariate trend is predicted using an enhanced pattern matching model, while the high-frequency fluctuation is modelled using Informer. Finally, the outcomes are combined to generate multi-step predictions. To validate the performance of the proposed model, we observed its performance on three real-world datasets, including Brent crude oil prices, European Union Allowance future prices, and Standard & Poor’s 500 index. Results indicate that the model surpasses all the benchmark models in terms of multiple evaluation metrics and forecast ranges, highlighting its effectiveness and robustness in multi-step forecasting.

Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method

Abstract. The Bayesian model averaging (BMA), hydrological uncertainty processor (HUP), and HUP-BMA methods have been widely used to quantify flood forecast uncertainty. This study proposes the copula-based hydrological uncertainty processor BMA (CHUP-BMA) method by introducing a copula-based HUP in the framework of BMA to bypass the need for a normal quantile transformation of the HUP-BMA method. The proposed ensemble forecast scheme consists of eight members (two forecast precipitation inputs; two advanced long short-term memory, LSTM, models; and two objective functions used to calibrate parameters) and is applied to the interval basin between the Xiangjiaba and Three Gorges Reservoir (TGR) dam sites. The ensemble forecast performance of the HUP-BMA and CHUP-BMA methods is explored in the 6–168 h forecast horizons. The TGR inflow forecasting results show that the two methods can improve the forecast accuracy over the selected member with the best forecast accuracy and that the CHUP-BMA performs much better than the HUP-BMA. Compared with the HUP-BMA method, the forecast interval width and continuous ranked probability score metrics of the CHUP-BMA method are reduced by a maximum of 28.42 % and 17.86 % within all forecast horizons, respectively. The probability forecast of the CHUP-BMA method has better reliability and sharpness and is more suitable for flood ensemble forecasts, providing reliable risk information for flood control decision-making.

Assessment of Relationship Between Human Resource Planning Strategies and Job Satisfaction in the Lake Region Economic Bloc County Governments in Kenya

Human resource planning encompasses predicting quantity, quality and category of human resources requirements in organizations to ensure that they are suitably redeployed and utilized optimally by organizations. Accurate predictions ease the rationalization of staff to improve its performance and create a conducive working environment. In that regard, organizations in the public and private sectors are formulating and deploying several strategies to enhance employee job satisfaction and gain a competitive edge. Despite the emphasis on job satisfaction, a review of available literature indicated limited focus on the relationship between human resource planning strategies and job satisfaction. This paper assessed the relationship between human resource planning strategies and job satisfaction in Kenya's Lake Region Economic Bloc County Governments. The study used the equity theory and the target population was all employees engaged by the 14 counties in Kenya's Lake Region Economic Bloc County Governments. Three counties: Kisumu, Bomet and Kakamega, which had employed 14,361 workers were sampled. The study sample consisted of 374 respondents, who arrived at the study using the Morgan and Krejcie sampling formula. A questionnaire was used to collect data analysed using SPSS for descriptive and inferential statistics and presented using tables. Regression analysis indicated that human resource planning strategies are positively and significantly related to job satisfaction (B = 0.939, p = .000). Thus, the study concluded that a significant relationship exists between human resource planning strategies and job satisfaction in Kenya's Lake Region Economic Bloc County Governments. The study recommends that county governments in the Lake Region Economic Bloc in Kenya emphasize implementing forecasting and restructuring strategies objectively and rationally to enhance employees' job satisfaction

TFFS: A trainable federal fusion strategy for multistep time series forecasting

This paper presents a trainable federal fusion strategy (TFFS) for multistep time series forecasting (MSTSF) that unifies the current strategies. First, we designed a feature fusion embedding scheme (FFES) that integrates sharing features and all future steps in a trainable mode to overcome the drawback of cumulative errors. Second, multitask learning was designed to train each step in a parallel mode, enabling each step to share a common feature with the others while keeping each step’s personality. Finally, a multiple-input multiple-output scheme (MIMOS) was developed to forecast future step values in semi-series mode, which increases the flexibility. Owing to this design, TFFS provides the following advantages for MSTSF: 1) TFFS is free of cumulative errors in future horizons during training. 2) TFFS provides accurate multistep forecasting results, requiring only one model training. 3) Sharing common features while retaining each step’s personality increases forecasting accuracy. We conducted many experiments on univariate and multivariate time-series datasets to validate the effectiveness of TFFS with seven common deep learning structures. The experimental results confirmed the state-of-the-art performance of the proposed method. Additionally, we extensively and systematically compared each strategy for MSTSF with deep learning.

Stock Prices Forecasting and Optimization Strategies Based on Support Vector Machines

With the global trend of digitization gaining prominence, the usage of machine learning methods such as Support Vector Machines and Reinforcement Learning for stock price prediction is becoming a hot topic. Over the past 40 years, China's economic market has undergone significant changes since the country's reform and opening up. In this study, the closing price and return of China's CSI 300 stock index are used as the database, and various data processing methods such as wavelet domain denoising, RSI screening and various SVM model optimization methods such as grid search and cross-validation are used to predict the upward or downward trend of stocks on the day after. The results of the study are presented by the model evaluation report and the heat map of the confusion matrix, which shows that the model prediction accuracy is 61% with the default parameters, and the accuracy improves to 67% after optimization. The results indicate that support vector machines are effective in stock price prediction, but there is still room for further improvement. This paper offers a potential approach that can increase return on investment and assist investors and financial institutions in making more informed investment decisions.