Abstract
Real-world time series present patterns that change over time, making them difficult to forecast using only one forecasting model. Dynamic selection approaches have been highlighted in literature due to their accuracy and ability to model different local patterns. These approaches select one or more models from a pool (or ensemble) to forecast each test pattern. This selection is performed based on the pool’s performance in a Region of Competence (RoC), a set of samples most similar to a test pattern. The RoC definition, the pool creation, the number of selected models, and the function for combining the forecasts are critical issues for the dynamic selection approaches once their accuracy is closely related to them. This paper proposes a dynamic selection system based on a heterogeneous pool that performs a data-driven choice to determine: (i) the best RoC size, (ii) the set of the most competent forecasting models, and (iii) the most suitable combination function. The selection uses an RoC composed of the nearest antecedent windows to a test pattern. The proposal employs a heterogeneous pool comprising six forecasting models: Autoregressive Integrated Moving Average (ARIMA), Theta model, Support Vector Regression (SVR), Multilayer Perceptron (MLP), Extreme Learning Machine (ELM), and Long Short-Term Memory (LSTM). An experimental analysis performed using seven well-known data sets showed that the proposal overcame literature single and ensemble approaches, indicating that it is able to perform a better dynamic selection.
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Matos, G.M., de Mattos Neto, P.S.G. (2023). An Intelligent Dynamic Selection System Based on Nearest Temporal Windows for Time Series Forecasting. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14259. Springer, Cham. https://doi.org/10.1007/978-3-031-44223-0_14
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