I am new to Python. I am using statsmodels for predicting through ARIMA. I have 3 questions. First of all, I am writing the following code
dates = pd.date_range('2012-07-09','2012-07-30')
series = [43.,32.,63.,98.,65.,78.,23.,35.,78.,56.,45.,45.,56.,6.,63.,45.,64.,34.,76.,34.,14.,54.]
res = Series(series, index=dates)
r = ARIMA(res,(1,2,0))
pred = r.predict(params = ? , start = ?, end = ? , typ='levels')
Here, what would be the params, start and end ? In the documentation I understood that start and end means the start and end of the predicting values but what about params. I do not know what i have to put in params.
Secondly, I have almost 1000 time series of items. So, for each item i want to recognize optimal (p,d,q) values and apply ARIMA each time in a loop so that at the end I get the result in a dictionary containing item name and its predicted value.Please help me how can do this ?
Third, Now If I am able to get ARIMA forecast for each time series it will not be just a forecast, instead it will contain lots of values like coeff, p-values, etc so how can I directly access the coeffients only and use them somewhere as values.
I'll be extremely thankful If someone can help me out in this. Many Thanks in advance.
Start and end are the starting and ending points you wish to forecast. So this might be start = '2012-07-31' and end = '2012-09-01'.
Regarding params - when .fit() is called, an ARIMAResults class is returned. This class' predict method does not require the params argument: start and end should be all that's needed.
For your second question, this answer should help. I was not actually able to get that code to work for myself, but I'm sure you could get a AIC/BIC grid search to work in that or a similar way. An alternative would be switching to R and using the auto.arima function, which also selects the best (p,d,q) order based on AIC/BIC (which is definitely more advisable than selecting based on p-values).
You should be able to get the coefficients from your fitted model using r.params