K. Koparanov, K. Georgiev, Vasil A. Shterev, D. Minkovska
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Improving Predictions of Long Sequences by Hyperparameter Tuning
The problem of forecasting long sequences is important in many different domains. Proper selection of the hyperparameters when a machine learning approach is applied could make the difference between adequate and inadequate model. Several algorithms for automatic hyperparameters tuning were evaluated and compared with baseline selection. As a result, recommendations have been made. Some of the intuitive assumptions for the baseline model proved to be wrong.
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