orecasting with Competing Models of Daily Bitcoin Price in R
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Date
2022
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Abstract
Bitcoin price exhibits patterns predictable on its historical pasts. We adopt ARIMA(auto), ARIMA(fix)
models and the Holt-Winters filter (HWF) with trend plus additive seasonal HWF (𝛾[0,1]), and no
seasonality HWF (𝛾[False]) to forecast the price of Bitcoin under three datasets–Actual (observed),
Polynomial (fitted) and STL-Trend (fitted). We apply daily time-series from 1/09/2014–28/12/2020,
and establish 18 models to forecast the price of Bitcoin. The results show that HWF (𝛾[0,1]) with lower
limit fitted on STL-Trend provides the best prediction on the first training-sample, while ARIMA(fix)
fitted on actual data outperform in the second training-set with the smallest Mean-Absolute-Error
(MAE). The training-set forecast performance of the ARIMA(fix) for the actual function provides better
performance with the least MAE. The HWF is appropriate for prediction of the daily Bitcoin price with
the generalise STL-Trend function, but ARIMA(fix) is more accurate for the actual series.