2026· International Workshop on Computer Modeling and Intelligent Systems· pp. 189-200· 0 citations· 41 references
Computer Science
TL;DR
Experimental results demonstrate that the proposed input space optimization strategy leads to a significant reduction in forecast error, and these improvements are achieved without increasing model complexity or changing the training procedure.
Long Horizon Time Series Forecasting (LHTSF) remains a challenging research problem due to the difficulty of producing reliable predictions over long forecasting horizons. Existing approaches mainly rely on recursive forecasting or multi-input multi-output (MIMO) strategies. Recursive methods are prone to error accumulation, while MIMO approaches may fail to adequately preserve temporal dependencies across forecast steps. In this study, we propose BlockMIMO, a block-based forecasting framework that provides a balance between these approaches. BlockMIMO divides the forecasting horizon into discrete blocks and predicts each segment sequentially while updating the input window with the generated blocks. This structure aims to preserve local temporal dependencies while mitigating error propagation in long-term forecasting. The proposed framework can be applied to different neural network architectures, and demonstrates superior performance on widely used multivariate time series datasets.
Ferayenur Bozkurt, Ceyhun E. Öztürk, G. Dogan et al.· Signal Processing and Commun...· 0 citations
Accurate forecasting in volatile and structurally complex demand environments requires models capable of capturing evolving trends, seasonality, nonlinear behavior and externally driven fluctuations. This study aims to propose the iterative ensemble forecasting with residual integration (IEFRI) framework, a structured ensemble methodology that improves forecast accuracy by explicitly and iteratively learning from residual errors rather than treating them as random noise. Unlike conventional one-stage ensemble approaches, IEFRI uses a multistage refinement process in which forecasts from heterogeneous base models are progressively corrected through successive neural and regression-based residual-learning cycles, allowing both nonlinear and linear error structures to be captured systematically. The framework integrates Holt–Winters exponential smoothing, seasonal auto regressive integrated moving average (SARIMA), artificial neural networks (ANNs) and multiple linear regression (MLR) within a decomposition-driven architecture. IEFRI is empirically validated using 25 years of monthly demand data (April 1998–March 2024) covering all major petroleum products in India – petrol, diesel, Liquified Petroleum Gas (LPG), jet fuel and kerosene – each exhibiting strong seasonality, trend shifts, structural change and sensitivity to macroeconomic and policy factors. Comparative evaluation using standard accuracy measures shows that IEFRI consistently outperforms individual models and widely used ensemble techniques such as bagging, boosting, random forests and stacked regression. While demonstrated using petroleum demand, the framework is generalizable to other complex and uncertainty-prone time-series forecasting applications.
This study develops and evaluates the IEFRI framework for complex demand time series. The approach combines heterogeneous base forecasting models – Holt–Winters exponential smoothing, SARIMA, ANNs and MLR – within a decomposition-driven ensemble structure. Initial forecasts are aggregated and subsequently refined through a multistage residual-learning process that integrates neural-network-based nonlinear correction and iterative regression-based linear adjustment. Model performance is assessed using rolling-origin out-of-sample evaluation and standard accuracy measures mean absolute percentatge error (MAPE), symmetric mean absolute percentage error (SMAPE), root mean square error (RMSE). The framework is empirically validated using 25 years of monthly demand data across five petroleum products, enabling robust comparison with conventional ensemble methods.
The empirical results show that the proposed IEFRI framework consistently improves forecast accuracy across all five petroleum products – petrol, diesel, LPG, jet fuel and kerosene – each characterized by strong seasonality, structural change and nonlinear dynamics. Compared with individual base models and conventional ensemble techniques such as bagging, boosting, random forests and stacked regression, IEFRI achieves lower forecasting errors under rolling-origin evaluation. The iterative residual-learning mechanism is found to be effective in systematically reducing both nonlinear and linear forecast errors across successive refinement stages. Performance gains are stable across products with differing demand characteristics, demonstrating the robustness of the framework. These findings indicate that explicitly modeling and reintegrating residual information enhances ensemble forecasting accuracy in complex, volatile demand environments.
Despite its strengths, this study has several limitations. First, the analysis relies on secondary data compiled from multiple sources, requiring interpolation and harmonization, which may introduce minor measurement bias. Second, the IEFRI framework is validated using historical monthly data from a single domain, and its generalizability to other sectors and higher-frequency data has not been empirically tested. Third, while the iterative residual-learning process improves accuracy, it increases computational complexity and may be sensitive to data quality and parameter settings. Finally, the study does not incorporate probabilistic or uncertainty forecasting, which could further enhance decision support in practice.
The proposed IEFRI framework provides practitioners with a systematic and robust approach for improving forecast accuracy in environments characterized by volatility, nonlinearity and structural change. By explicitly learning from residual errors, the model supports more reliable demand forecasts that can enhance operational planning, capacity allocation, inventory management and medium-term strategic decision-making. The framework is flexible and can be integrated with existing forecasting systems that already use statistical or machine learning models, allowing organizations to improve performance without fully replacing current tools. For policymakers and managers, more accurate forecasts enable better resource planning, risk mitigation and policy evaluation in sectors where demand uncertainty has significant economic and operational consequences.
This study is based on original research and contributes original value by introducing the IEFRI framework, which advances ensemble forecasting beyond conventional one-stage aggregation. Unlike standard ensemble methods that treat residuals as random noise, IEFRI explicitly and iteratively models residual errors through a structured combination of neural-network-based nonlinear learning and regression-based linear correction. This multistage residual integration provides a systematic mechanism for continuous forecast refinement. The study has been subjected to extensive empirical validation using the demand for various petroleum products which exhibit multiple, structurally diverse demand series. The framework offers a reusable, domain-agnostic methodology for improving forecast accuracy in complex, volatile time-series environments.
Ramesh Murthy· Journal of Modelling in Mana...· 0 citations
The literature is organized around five recurring difficulties: nonlinear and nonstationary behavior, contamination and structural breaks, uncertainty, long contexts and cross-variable dependence, and limited target-domain data.
Chu-Ting Wen· Applied and Computational En...· 0 citations
To better forecast mixed-frequency time series, it is the key to choose a suitable way for frequency alignment. However, the existing methods are all limited to linear transformations, and this may overlook the possible nonlinearity, leading to a worse prediction. We alternatively consider a deep neural network for each frequency alignment, and hence a depth-separable neural network. Moreover, a parameter-sharing mechanism is adopted across the alignment at each stage, making possible a deeper network for a large set of higher-frequency predictors. This paper establishes an approximation theory for the proposed depth-separable network, and a non-asymptotic prediction error bound is also derived. Simulation studies demonstrate the finite-sample performance of the proposed method, and an empirical application to forecasting U.S. quarterly macroeconomic variables using monthly and daily indicators, highlights its superior predictive accuracy over existing mixed-frequency methods.
Interdependent operating states, rather than individual load curves, are increasingly required for short-term power system prediction. This study formulates and tests a common mathematical-computational approach for one-step-ahead prediction of demand, generation, load shedding, and imbalance derived from them within a dynamic grid environment. Operational data were archived as hourly data, prepared chronologically, transformed into a multivariate feature space, and split into training, validation, and test sets. The proposed framework is tested against naïve persistence, Ridge regression, Random Forest, XGBoost, and the VAR models separately and is based on a vector autoregressive mathematical core and an XGBoost residual-correction layer. In the results, the model's effectiveness is target-dependent. The hybrid framework consistently performed best for generation and was statistically similar to the advanced models for demand, load shedding, and derived imbalance. The mathematically derived variables, source-composition features, and short-term dynamic indicators were found to have different contribution values for each target in ablation and feature-importance analyses. Stressed load-shedding conditions were also found to have lower predictive accuracy in regime-specific testing. These results show that mathematically constrained residual learning is useful for coherent forecasting of continuous operating states, while sparse stress-related variables necessitate extensions to the model to learn them in an event-aware fashion. The study offers a replicable methodology for predictive analysis of a shorter time horizon in the context of grid operation.
Pradeep Kumar H S, H. S· Future Technology· 0 citations
It is argued that modern methods make progress by expanding the classical forecasting template: they allow more flexible dynamics, use larger information sets and training corpora, and represent richer predictive distributions, yet they often lack the inferential and structural tools that make classical models useful for testing, explanation, and policy analysis.
