Let’s say I have figured out my stationary series after the appropriate transformations of the time series data.
Lets say my transformations are
- Series a = Take the change in % from the last value and then
- Series b = Take a EWMA (Series a)
- Subtract the Series b from Series a to get Series C.
And Series C is the stationary series…
Now, my understanding is:
In order to fit the model ARIMA, I should be using Series c.
However, I went through the Time Series forecasting article on analytics vidhya, and found the ARIMA fitting parameters a little confusing.
They were seen using series b , even series c and what confused me was also the plotting of a series and as against the fitted values of the mode.
Eg:( from the article)
model = ARIMA(ts_log, order=(0, 1, 2))
results_MA = model.fit(disp=-1)
plt.title(‘RSS: %.4f’% sum((results_MA.fittedvalues-ts_log_diff)**2))
Here its fittong on ts_log and plotting ts_log_diff
Why is the fitting and plotting on different series’ if it is for the purpose of mere visualization and not predictions ?
Can you please help to confirm on this ambiguity?