Frequency-based ensemble forecasting model for time series forecasting
Peer reviewed, Journal article
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Original versionSaeed, W. (2022). Frequency-based ensemble forecasting model for time series forecasting. Computational & Applied Mathemathics, 41 (2), 17. https://doi.org/10.1007/s40314-022-01765-x
The M4 forecasting competition challenged the participants to forecast 100,000 time series with different frequencies: hourly, daily, weekly, monthly, quarterly, and yearly. These series come mainly from the economic, finance, demographics, and industrial areas. This paper describes the model used in the competition, which is a combination of statistical methods, namely auto-regressive integrated moving-average, exponential smoothing (ETS), bagged ETS, temporal hierarchical forecasting method, Box-Cox transformation, ARMA errors, Trend and Seasonal components (BATS), and Trigonometric seasonality BATS (TBATS). Forty-nine submissions were evaluated by the organizers and compared with 12 benchmarks and standards for comparison forecasting methods. Based on the results, the proposed model is listed among the 17 submissions that outperform the 12 benchmarks and standards for comparison forecasting methods, ranked 15th on average and 4th with the weekly time series. In addition, a further comparison was conducted between the proposed model and other forecasting methods on forecasting EUR/USD exchange rate and Bitcoin closing price time series. It is apparent from the results that the proposed model can produce accurate results compared to many forecasting methods.