In recent years, various new Machine Learning and Deep Learning algorithms have been introduced, claiming to offer better performance than traditional statistical approaches when forecasting time series. Studies seeking evidence to support the usage of ML/DL over statistical approaches have been limited to comparing the forecasting performance of univariate, linear time series data. This research compares the performance of traditional statistical-based and ML/DL methods for forecasting multivariate and nonlinear time series.
Bonilla, Miguel E. Jr.; McDonald, Jason; Toth, Tamas; and Sadler, Bivin
"Traditional vs Machine Learning Approaches: A Comparison of Time Series Modeling Methods,"
SMU Data Science Review: Vol. 7:
2, Article 2.
Available at: https://scholar.smu.edu/datasciencereview/vol7/iss2/2
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