Hi Friends,

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.plot(ts_log_diff)

plt.plot(results_MA.fittedvalues, color=‘red’)

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?