Official Journal of the Asia Oceania Geosciences Society (AOGS)
Table 4 Univariate time series forecasting methods of this study (part 1): stochastic methods
From: One-step ahead forecasting of geophysical processes within a purely statistical framework
s/n | Abbreviated name | Category | R algorithm(s) | Implementation notes |
|---|---|---|---|---|
1 | Naïve | Simple | ||
2 | RW | rwf {forecast} | drift = TRUE | |
3 | ARIMA_f | AutoRegressive Integrated Moving Average (ARIMA) | Arima {forecast}, forecast {forecast} | Arima {forecast}: include.mean = TRUE, include.drift = FALSE, method = ”ML” |
4 | ARIMA_s | Arima {forecast}, simulate {stats} | ||
5 | auto_ARIMA_f | auto.arima {forecast}, forecast {forecast} | ||
6 | auto_ARIMA_s | auto.arima {forecast}, simulate {stats} | ||
7 | auto_ARFIMA | AutoRegressive Fractionally Integrated Moving Average (ARFIMA) | arfima {forecast}, forecast {forecast} | arfima {forecast}: estim = ”mle” |
8 | BATS | State space | bats {forecast}, forecast {forecast} | |
9 | ETS_s | ets {forecast}, simulate {stats} | ||
10 | SES | Exponential smoothing | ses {forecast} | |
11 | Theta | thetaf {forecast} |