Jul 2026· Signal Processing and Communications Applications Conference· pp. 1-4· 0 citations· 16 references
Abstract
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.
Solar irradiance forecasting is key to reliable smart grid operation under high photovoltaic penetration. However, the interaction between historical input window length, forecasting horizon, and learning strategy remains insufficiently understood. In this study, we investigate the coupling between temporal window size...
Soumyabrata Dev· International Conference on...· 0 citations
In order to make well-informed decisions, long-term time series forecasting is crucial for a number of applications in finance, energy, and environmental science. While traditional transformer models have demonstrated a strong ability to capture temporal dependencies, they frequently struggle to handle the lengthy sequ...
YongKyung Oh, Alex A. T. Bui· IISE Annual Conference &...· 0 citations
Accurate long-term time series forecasting remains a challenging task in various real-world applications, requiring the consideration of high-dimensional features. Traditional forecasting methods often rely on manual feature selection, which can be time-consuming and subjective and may not capture the intricate relatio...
Ali Sarabi, Arash Sarabi, G. Runger· IISE Annual Conference &...· 0 citations
A forecasting framework that explicitly models the coupling between trend and seasonality, and a heteroscedastic Laplace loss function that combines uncertainty weighting with heteroscedastic modeling, reducing the impact of error accumulation over long prediction horizons and enhancing robustness to outliers and heavy...
Zi-Qiong Li, He-Yu Chai, Xinru Liu et al.· IEEE Transactions on Neural...· 0 citations
Multivariate time series forecasting (MTSF) is critical across many real-world domains. Existing deep learning approaches fall into two paradigms with distinct limitations: channel-independent (CI) methods unconditionally ignore cross-variable dependencies and model only temporal dynamics, while channel-dependent (CD)...
Yu-Cheng Wu, Fan-Keng Sun, Li-Chun Lu et al.· 0 citations
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