TL;DR
Empirical results demonstrate that the incorporation of seasonal-trend decomposition and a second-order GRU structure significantly improves the accuracy of long-term time series forecasting.
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...
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...
Accurate short-term net load forecasting is challenging due to the superimposition of meteorological influences, daily and weekly seasonal patterns, and irregular disturbances in the observed series. On the one hand, redundant meteorological input data risks hindering the learning process. On the other hand, methods th...
SARIMA’s versatility in capturing weekly seasonal patterns, as observed in the 7-day cycles within the COVID-19 data, which reflect reporting or behavioral trends, is highlighted.
The LSTM model’s superior accuracy supports its integration into Building Energy Management Systems (BEMS) for demand response, anomaly detection, and predictive control, enabling professionals to reduce operational energy costs, enhance occupant comfort, and advance sustainability targets within modern building portfo...
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