Python: SARIMAX Model Fits too slow
I have a time series data with the date
and temperature
records of a city. Following are my observations from the time series analysis:
- By plotting the graph of
date
vstemperature
seasonality is observed. - Performing
adfuller test
we find that the data is already stationary, sod=0
. - Perform Partial Autocorrelation and Autocorrelation with First Seasonal Difference and found
p=2
andq=10
respectively.
Code to Train Model
model=sm.tsa.statespace.SARIMAX(df['temperature'],order=(1, 1, 1),seasonal_order=(2,0,10,12))
results=model.fit()
This fit
function runs indefinitely and does not reach an output. I am running on a on Google Colab CPU.
How to fix this issue?
Topic colab python-3.x arima time-series python
Category Data Science