Suppose I have a non-stationary limited data. Do I have to make it stationary before making forecasts? Can I use exponential smoothing, moving averages or even Holt Winters methods without making my data stationary? I've read that time series should be stationary to use such methods? Is that correct?
Solved – Non-Stationary Time Series Forecasting
exponential-smoothingstationaritytime series
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Best Answer
I don't know what "non-stationary limited data" means. So I will assume you mean "non-stationary data".
Exponential smoothing methods including Holt-Winters methods are appropriate for (some kinds of) non-stationary data. In fact, they are only really appropriate if the data are non-stationary. Using an exponential smoothing method on stationary data is not wrong but is sub-optimal.
If by "moving averages", you mean forecasting using a moving average of recent observations, then that is also ok for some kinds of non-stationary data. But it obviously won't work well with trends or seasonality.
If by "moving averages", you mean a moving average model (i.e. a model consisting of a linear combination of past error terms), then you do need a stationary time series.