Improving Multiple Time Series Forecasting with Data Stream Mining Algorithms

This paper proposes a hybrid ensemble learning approach that combines statistical and data stream mining algorithms to obtain better forecasting performance in multiple time series prediction problems. Although some multiple time series algorithms perform surprisingly well in a variety of domains, i...

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Bibliographic Details
Published in:Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics pp. 1060 - 1067
Main Authors: Mochinski, Marcos Alberto, Paul Barddal, Jean, Enembreck, Fabricio
Format: Conference Proceeding
Language:English
Published: IEEE 11.10.2020
Subjects:
Data mining
data stream mining algorithms
Forecasting
hybrid ensemble
multiple time series
Prediction algorithms
Real-time systems
Social networking (online)
Time series analysis
Time series forecasting
Training
ISSN:2577-1655
Online Access:Get full text
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Summary:This paper proposes a hybrid ensemble learning approach that combines statistical and data stream mining algorithms to obtain better forecasting performance in multiple time series prediction problems. Although some multiple time series algorithms perform surprisingly well in a variety of domains, it is well-known that no one is dominant for every existent domain. Therefore, we developed a meta-technique based on data stream mining and static ensemble selection strategy and evaluated its forecasting goodness-of-fit in time series datasets from M3 and M4 competitions. After training different regression models, we show how the combination of auto.arima and AdaGrad leads to improved forecasting rates, thus surpassing the results of state-of-art algorithms.
ISSN:2577-1655
DOI:10.1109/SMC42975.2020.9283059