I am trying to predict stock price of a company, the data is non stationary.
Steps I followed -
- Analyze the raw data
- Determine whether the raw time series data is stationary or not using ADF and KPSS
- Applied first differencing and seasonal differencing to make the data stationary
- Determine the MA and AR lags using the stationary data by plotting ACF, PACF plots
My question is should I pass raw data (non-stationary, from Step 1) to time series model like SARIMA, ARIMA and SARIMAX and use the stationary data(Step 3) to determine MA and AR lag coefficients for the model
I should pass the stationary data(Step 3) to the time series model like SARIMA, ARIMA, SARIMAX, etc. and use the MA and AR lag coefficients for the model. And then to determine the predicted original time series , I should undo all the transformations that I did in Step 3 to make the time series data stationary.
Thank you for your help