Auto ARIMA parameters for correct forecasting

Question

I want to find correct Auto ARIMA values for my dataset. Since my values are presented hourly, I couldn't estimate the parameters. The problem should be about 'm', but greater values crashes eventually. I also tried seasonal false, which resulted with linear forecast.

model = pm.auto_arima(df.value, start_p=1, start_q=1,
                      test='adf',       # use adftest to find optimal 'd'
                      max_p=3, max_q=3, # maximum p and q
                      m=1,              # frequency of series
                      d=None,           # let model determine 'd'
                      seasonal=False,   # No Seasonality
                      start_P=0, 
                      D=0, 
                      trace=True,
                      error_action='ignore',  
                      suppress_warnings=True, 
                      stepwise=True)

print(model.summary())

Answer 1

Your data is clearly seasonal, so you should set the parameter seasonal = True.

m is the length of a seasonal period, meaning the number of data points in each period. You have multiple seasonalities in your data (daily, weekly and probably yearly), but I think you should focus on the daily seasonality. Since you have hourly data, each season has 24 data points. Therefore, you should set m = 24. While ARIMA may struggle with long seasonalities, I think that 24 should be fine.

I would not restrict or lock ARIMA to specific values/ranges for each parameter. Try the following:

model = pm.auto_arima(df.value, 
                      test='adf',
                      seasonal=True,
                      m=24,
                      trace=True,
                      error_action='ignore',  
                      suppress_warnings=True, 
                      stepwise=True)