M3
class will automatically download the complete M3 dataset and
process it.
It return three Dataframes: Y_df
contains the values for the target
variables, X_df
contains exogenous calendar features and S_df
contains static features for each time-series. For this example we will
only use Y_df
.
If you want to use your own data just replace Y_df
. Be sure to use a
long format and have a simmilar structure than our data set.
1_000
series to speed up
computations. Remove the filter to use the whole dataset.
MLForecast.fit
method you can train a set of models to your dataset. You can modify the
hyperparameters of the model to get a better accuracy, in this case we
will use the default hyperparameters of lgb.LGBMRegressor
.
MLForecast
object has the following parameters:
models
: a list of sklearn-like (fit
and predict
) models.freq
: a string indicating the frequency of the data. See panda’s
available
frequencies.differences
: Differences to take of the target before computing
the features. These are restored at the forecasting step.lags
: Lags of the target to use as features.differences
and lags
to produce
features. See the full
documentation to see
all available features.
Any settings are passed into the constructor. Then you call its fit
method and pass in the historical data frame Y_df_M3
.
AirPassengers
with
the
MLForecast.predict
method, we just have to pass the new dataframe to the new_data
argument.
unique_id | ds | LGBMRegressor | |
---|---|---|---|
0 | AirPassengers | 1960-01-01 | 422.740096 |
1 | AirPassengers | 1960-02-01 | 399.480193 |
2 | AirPassengers | 1960-03-01 | 458.220289 |
3 | AirPassengers | 1960-04-01 | 442.960385 |
4 | AirPassengers | 1960-05-01 | 461.700482 |
mae
).