Jul 2026· Scientia. Technology, Science and Society· Vol 3, pp. 68-74· 0 citations· 9 references
TL;DR
This work provides a thorough analysis of deep learning architectures, contemporary machine learning algorithms, and traditional statistical models for time series forecasting, including ARIMA, Support Vector Regression, Random Forests, Long Short-Term Memory, and Transformer-based methods.
Abstract
In many fields, such as banking, healthcare, energy, and climate analysis, where precise predicting of future values is critical for making decisions, time series forecasting plays a critical role. With the quick development of machine learning methods, data-driven approaches have supplemented and frequently surpassed classic statistical models. This work provides a thorough analysis of deep learning architectures, contemporary machine learning algorithms, and traditional statistical models for time series forecasting. Critical analysis is done on important models including ARIMA, Support Vector Regression, Random Forests, Long Short-Term Memory (LSTM), and Transformer-based methods. The study also looks at evaluation criteria and benchmark datasets that are frequently used to compare performance. To illustrate how these models might be used in actual forecasting situations, a case study is provided. Problems including data quality, model interpretability, and computational complexity still exist despite tremendous advancements. Lastly, future directions are considered, such as explainable forecasting systems, automated machine learning, and hybrid models. An organized overview of contemporary developments and difficulties in time series forecasting is offered by this review.
This study compares ARIMA, LSTM, and temporal fusion transformer (TFT) models across three applications and shows that TFT consistently achieved superior forecasting performance and demonstrated greater robustness to increasing missingness, while k-NN generally provided the most effective imputation performance across datasets.
M. Hosseini, Mohamad Forouzanfar· Computer Science and Informa...· 0 citations
The findings demonstrate that rigorous leakage-free validation is essential for reliable forecasting research and that, for monthly Robusta coffee prices, increased model complexity does not necessarily yield superior predictive performance.
Dler H Kadir, D. Khalil, Azhin M. Khudhur· Forecasting· 0 citations
Forecasting time series over long horizons is essential for proactive decision-making in many systems. Recent research has focused on transformer-based architectures, which capture long-range dependencies in sequential data. However, several studies show that simpler linear models can outperform transformers by avoiding overfitting during training. In this context, we present NeuroFlexMLP, a deep learning model for multivariate time series forecasting tasks. NeuroFlexMLP's key distinct feature is the adaptability to the diverse complexity of real-world time series, which is achieved, from the architecture standpoint, by adding non-linear residual blocks to a first linear block. This architectural design simplifies hyperparameter optimization, leading to accurate forecasts for various time series data types regardless of the lookback or prediction horizons, outperforming state-of-the-art (SOTA) models on challenging real-world datasets. Its Multi-Layer Perceptron (MLP) design ensures high computational efficiency, making it scalable for longer input sequences than transformer-based models. We validate NeuroFlexMLP for the LEO satellite beam hopping use case, where its lightweight design enables on-board deployment, and on state-of-the art AI datasets. Across all these benchmarks, NeuroFlexMLP achieves competitive accuracy over state-of-the-art models while providing an adaptive architecture that significantly reduces computational overhead. On the LEO beam hopping task, it achieves up to 35.9% MSE reduction over Informer, which translates into up to 28% lower provisioning cost under asymmetric cost models that penalize under-allocation more heavily than over-allocation.
P. F. Pérez, Claudio Fiandrino, Marco Fiore et al.· La Main· 0 citations
Forecasting serves as a critical cornerstone for strategic planning, operational efficiency, and risk mitigation across modern civilization. By converting historical data into actionable forward-looking insights, it enables organizations and governments to anticipate market shifts, optimize resource distribution, and safeguard against systemic uncertainties. Predictive modeling is a fundamental task in many fields, such as finance, economics, engineering, and artificial intelligence. The aim of this paper is to summarize the methods of statistics and machine learning, outline their inherent challenges, and project future research directions. This paper mainly discusses traditional statistical methods (including Autoregressive Integrated Moving Average [ARIMA] and regression analysis), machine learning approaches (such as Random Forest and Support Vector Machines [SVM]), and deep learning models (such as Long Short-Term Memory [LSTM] networks and hybrid time series-ML models). Nowadays, as these interconnected fields become increasingly complicated, practitioners face severe challenges regarding data quality, computational complexity, and mathematical interpretability. This paper comprehensively reviews these methodologies, establishes a comparative taxonomy, and delineates the evolutionary trajectory of future forecasting applications.
Zhi-Ting Chen· Applied and Computational En...· 0 citations
This survey re-examines deep learning models for MTS forecasting through the requirements of efficiency and explainability, and identifies key open challenges including the absence of standardized explainability benchmarks for time series, the interpretability gap in state space models, and the need to advance from correlational to causal explanations.
The USD/IDR exchange rate is a key daily barometer of Indonesia's economic health. Accurate forecasting is vital for trade, inflation, and monetary stability. However, its volatile and nonlinear dynamics pose challenges. While research has applied statistical models, machine learning, and deep learning, few studies offer a comprehensive comparison integrating predictive accuracy with model interpretability, particularly using banking stock prices as exogenous predictors. This study addresses this gap by developing and evaluating eight forecasting approaches—a naive random-walk baseline, two statistical models (ARIMA, SARIMAX), three tree-based ensembles (Random Forest, XGBoost, LightGBM), and two recurrent neural networks (LSTM, GRU) using daily data from January 2015 to July 2026 (3,005 observations). Five major banking stocks (BBCA, BBRI, BMRI, BBNI, BDMN) are included as one-day-lagged exogenous features. Models are assessed via hold-out testing and five-fold walk-forward cross-validation using RMSE, MAE, MAPE, and R². Contrary to expectations, the naive random-walk consistently achieves the lowest error (RMSE=115.24, MAE=63.80, MAPE=0.39%) and the most stable performance, with LSTM as the best-performing complex model (RMSE=269.59, R²=0.785). Diebold-Mariano tests confirm statistical significance (p<0.001). To enhance transparency, SHAP-based Explainable AI is applied to Random Forest, revealing that the lagged USD/IDR value overwhelmingly dominates predictions (mean |SHAP|=1,509.01), while banking stock contributions are negligible. These findings also empirically confirm the well-known Meese-Rogoff puzzle and weak-form market efficiency for USD/IDR, clearly proving that simple baselines remain formidable benchmarks for short-horizon forecasts. This study ultimately underscores the critical importance of combining rigorous benchmarking with XAI to deliver accurate and interpretable predictions for economic policymakers and financial practitioners.
D. Setyawan, Astrid Sulistya Azahra, Mugi Lestari· International Journal of Mat...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
