  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.
