Compute transformations on your exogenous features for MLForecast
ds | unique_id | price | |
---|---|---|---|
0 | 2000-10-05 | 0 | 0.548814 |
1 | 2000-10-06 | 0 | 0.715189 |
X_df
in
MLForecast.predict
.
If you want to use not only the price but the lag7 of the price and the
expanding mean of the lag1 for example, you can compute them before
training, merge them with your series and then provide the future values
through X_df
. Consider the following example.
ds | unique_id | price | price_lag7 | price_expanding_mean_lag1 | |
---|---|---|---|---|---|
0 | 2000-10-05 | 0 | 0.548814 | NaN | NaN |
1 | 2000-10-06 | 0 | 0.715189 | NaN | 0.548814 |
2 | 2000-10-07 | 0 | 0.602763 | NaN | 0.632001 |
3 | 2000-10-08 | 0 | 0.544883 | NaN | 0.622255 |
4 | 2000-10-09 | 0 | 0.423655 | NaN | 0.602912 |
5 | 2000-10-10 | 0 | 0.645894 | NaN | 0.567061 |
6 | 2000-10-11 | 0 | 0.437587 | NaN | 0.580200 |
7 | 2000-10-12 | 0 | 0.891773 | 0.548814 | 0.559827 |
8 | 2000-10-13 | 0 | 0.963663 | 0.715189 | 0.601320 |
9 | 2000-10-14 | 0 | 0.383442 | 0.602763 | 0.641580 |
unique_id | ds | y | price | price_lag7 | price_expanding_mean_lag1 | |
---|---|---|---|---|---|---|
0 | 0 | 2000-10-05 | 0.322947 | 0.548814 | NaN | NaN |
1 | 0 | 2000-10-06 | 1.218794 | 0.715189 | NaN | 0.548814 |
2 | 0 | 2000-10-07 | 2.445887 | 0.602763 | NaN | 0.632001 |
3 | 0 | 2000-10-08 | 3.481831 | 0.544883 | NaN | 0.622255 |
4 | 0 | 2000-10-09 | 4.191721 | 0.423655 | NaN | 0.602912 |
5 | 0 | 2000-10-10 | 5.395863 | 0.645894 | NaN | 0.567061 |
6 | 0 | 2000-10-11 | 6.264447 | 0.437587 | NaN | 0.580200 |
7 | 0 | 2000-10-12 | 0.284022 | 0.891773 | 0.548814 | 0.559827 |
8 | 0 | 2000-10-13 | 1.462798 | 0.963663 | 0.715189 | 0.601320 |
9 | 0 | 2000-10-14 | 2.035518 | 0.383442 | 0.602763 | 0.641580 |
unique_id | ds | y | price | price_lag7 | price_expanding_mean_lag1 | lag1 | dayofweek | |
---|---|---|---|---|---|---|---|---|
1 | 0 | 2000-10-06 | 1.218794 | 0.715189 | NaN | 0.548814 | 0.322947 | 4 |
2 | 0 | 2000-10-07 | 2.445887 | 0.602763 | NaN | 0.632001 | 1.218794 | 5 |
3 | 0 | 2000-10-08 | 3.481831 | 0.544883 | NaN | 0.622255 | 2.445887 | 6 |
4 | 0 | 2000-10-09 | 4.191721 | 0.423655 | NaN | 0.602912 | 3.481831 | 0 |
5 | 0 | 2000-10-10 | 5.395863 | 0.645894 | NaN | 0.567061 | 4.191721 | 1 |
dropna
argument only considers the
null values generated by the lag features based on the target. If you
want to drop all rows containing null values you have to do that in your
original series.
unique_id | ds | y | price | price_lag7 | price_expanding_mean_lag1 | lag1 | dayofweek | |
---|---|---|---|---|---|---|---|---|
8 | 0 | 2000-10-13 | 1.462798 | 0.963663 | 0.715189 | 0.601320 | 0.284022 | 4 |
9 | 0 | 2000-10-14 | 2.035518 | 0.383442 | 0.602763 | 0.641580 | 1.462798 | 5 |
10 | 0 | 2000-10-15 | 3.043565 | 0.791725 | 0.544883 | 0.615766 | 2.035518 | 6 |
11 | 0 | 2000-10-16 | 4.010109 | 0.528895 | 0.423655 | 0.631763 | 3.043565 | 0 |
12 | 0 | 2000-10-17 | 5.416310 | 0.568045 | 0.645894 | 0.623190 | 4.010109 | 1 |
unique_id | ds | LinearRegression | |
---|---|---|---|
0 | 0 | 2001-05-15 | 3.803967 |
1 | 1 | 2001-05-15 | 3.512489 |
2 | 2 | 2001-05-15 | 3.170019 |
3 | 3 | 2001-05-15 | 4.307121 |
4 | 4 | 2001-05-15 | 3.018758 |