Recency-Weighted Temporally-Segmented Ensemble for Time-Series Modeling
<|reference_start|>Recency-Weighted Temporally-Segmented Ensemble for Time-Series Modeling: Time-series modeling in process industries faces the challenge of dealing with complex, multi-faceted, and evolving data characteristics. Conventional single model approaches often struggle to capture the interplay of diverse dynamics, resulting in suboptimal forecasts. Addressing this, we introduce the Recency-Weighted Temporally-Segmented (ReWTS, pronounced `roots') ensemble model, a novel chunk-based approach for multi-step forecasting. The key characteristics of the ReWTS model are twofold: 1) It facilitates specialization of models into different dynamics by segmenting the training data into `chunks' of data and training one model per chunk. 2) During inference, an optimization procedure assesses each model on the recent past and selects the active models, such that the appropriate mixture of previously learned dynamics can be recalled to forecast the future. This method not only captures the nuances of each period, but also adapts more effectively to changes over time compared to conventional `global' models trained on all data in one go. We present a comparative analysis, utilizing two years of data from a wastewater treatment plant and a drinking water treatment plant in Norway, demonstrating the ReWTS ensemble's superiority. It consistently outperforms the global model in terms of mean squared forecasting error across various model architectures by 10-70\% on both datasets, notably exhibiting greater resilience to outliers. This approach shows promise in developing automatic, adaptable forecasting models for decision-making and control systems in process industries and other complex systems.<|reference_end|>
@article{johnsen2024recency-weighted,
    title={Recency-Weighted Temporally-Segmented Ensemble for Time-Series Modeling},
    author={P{aa}l V. Johnsen, Eivind B{o}hn, S{o}lve Eidnes, Filippo Remonato
  and Signe Riemer-S{o}rensen},
    journal={arXiv preprint arXiv:2403.02150},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.02150},
    primaryClass={stat.ML cs.LG}
}