I am new to time series modeling in R. I have sales data of one year and three months only. I am trying to do sales forecasting at the day level or max at the week level. Following is the step I intend to follow
- Convert it into time series object using
ts(data$qty, frequency= ??). Here I am very confused about frequency. I can see in data that there is some seasonality like sales is picking up in May, June, July and then again in festival seasons. I guess I cannot use 365 as I have only one year data. Please suggest what should be the frequency
- Decompose the time series. Subtract the seasonality and trend from the actual time series model
- Fit ARIMA to get a prediction
- Again add seasonality and trend to output the final forecast
Please provide feedback on this if its correct approach or not or if there is any other better way to handle it.