CODEWITHSUNDEEP
pythonrstatisticstime-seriesstatsmodels
Miriam Farber
Miriam Farber

Reputation: 19624

Conditional Forcasting with VAR model in python

I am using VAR model to forecast multivariate time series with lag 2. I have three features, and would like to forcast several timestamps forward. Instead of forcasting all the three features, I actually know the values of two of the features, and would like to forcast only one feature.

If I wanted to forcast all the three features 5 timestamps a head, I could have done that as follows (this is a toy example):

import pandas as pd
from statsmodels.tsa.api import VAR    
data=pd.DataFrame({'Date':['1959-06-01','1959-06-02','1959-06-03','1959-06-04']\
                   ,'a':[1,2,3,5],'b':[2,3,5,8],'c':[3,4,7,11]})
data.set_index('Date', inplace=True)
model = VAR(data)
results = model.fit(2)
results.forecast(data.values[-2:], 5)

Note that data is

            a  b   c
Date                
1959-06-01  1  2   3
1959-06-02  2  3   4
1959-06-03  3  5   7
1959-06-04  5  8  11

And the forecast gives me

array([[   8.01388889,   12.90277778,   17.79166667],
       [  12.93113426,   20.67650463,   28.421875  ],
       [  20.73343461,   33.12405961,   45.51468461],
       [  33.22366195,   52.98948789,   72.75531383],
       [  53.15895736,   84.72805652,  116.29715569]])

Let's say I knew that the next 5 values for a should have actually been 8,13,21,34,55 and the next 5 values for b should have been 13,21,34,55,89. Is there a way to incorporate that into the model in statsmodels.tsa (or any other python package) to forecast only the 5 values of c? I know that R has such an option, by incorporating "hard" conditions into cpredict.VAR, but I was wondering if this can be done in python as well.

The above is a toy example. In reality I have several dozens of features, but I still know all of them and would like predict only one of them using VAR model.

Upvotes: 4

Views: 2267

Answers (1)

Derek He
Derek He

Reputation: 21

I have a similar issue when solving this problem. This is a makeshift method to accomplish what you are asking.

prediction = model_fit.forecast(model_fit.y, steps=len(test))
predictions = prediction[:,0]

` Where 0 in the prediction[:,0] refers to the column that contains the desired forecasting value.

Upvotes: 1

Related Questions