I would need the ARIMA model as a baseline for dynamic regression models as described by Hyndman where I will include day dummies, holidays, months etc. A nice example of what I plan to do is question this QuestionArima Model with weekday dummy variables Forecast. Is my assumption correct that with these dummies I no longer need to correct for the seasonality?
MarianD
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Make daily business data stationary for ARIMA
For my master thesis I have a dataset with the daily count of orders from a company over ten years. Naturally this data follows strong seasonality with almost no orders on the weekend. To fit an ARIMA model, I hoped to make the data stationary using seasonal differences, but when I plot the differences the ACF and PACF still show significant spikes at the weekly intervals of 7, 14, etc.
library(forecast)
ggtsdisplay(diff(orders, 7), lag.max = 25)
The KPSS and Ljung Box test both tell that the data is stationary, but the plot worries me. I have tried different ways to transform the data:
- Differencing of higher order
- BoxCox Transformation
But the plot always shows similar results. Does anyone know the reason for this and how it could be fixed? I have attached a 3-year extract of the data below. As a novice in time series I lack the judgement to tell if it is ok to go ahead and I would highly appreciate your input.
I would need the ARIMA model as a baseline for dynamic regression models as described by Hyndman where I will include day dummies, holidays, months etc. A nice example of what I plan to do is this Question. Is my assumption correct that with these dummies I no longer need to correct for the seasonality?
Data
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orders <- ts(data, frequency = 365.25, start = c(2015,1))