Article ID Journal Published Year Pages File Type
10321777 Expert Systems with Applications 2015 30 Pages PDF
Abstract
Several empirical results on time series indicate that combining forecasts is, on average, better than selecting a single winning forecasting model. The success of the combination approach depends on how well the combination weights can be determined. Focusing on convex combinations - linear combinations with forecast weights constrained to be non-negative and to sum to unity - this paper proposes a new weight generation framework called Neural Expert Weighting (NEW). The framework generates dynamic weighting models based on neural networks, both relaxing in-sample performance dependence and abstracting statistical complexity. Assessed with 15 time series divided into two case studies - petroleum products and NN3 forecasting competition - the NEW models presented promising results.
Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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