Integrate multivariate time series forecasting (#254)

* Integrate multivariate time series forecasting, now supports
continuous and categorical variables

- update data.py to transform time series data
- update search space
- update documentations to reflect changes
- update test_forecast.py
- rename 'forecast' task to 'ts_forecast' task

* update automl.py and test_forecast.py

* update forecast notebook

* update README.md and setup.py

* update ml.py and test_forecast.py

- make "ds" and "y" constant variables

* replace constants with constant variables

* bump version to 0.7.0

* update setup.py
- support 'forecast' and 'ts_forecast'

* update automl.py and data.py
- support 'forecast' and 'ts_forecast' tasks

flaml/ml.py

@@ -33,7 +33,7 @@ from .model import (
     ARIMA,
     SARIMAX,
 )
-from .data import CLASSIFICATION, group_counts
+from .data import CLASSIFICATION, group_counts, TS_FORECAST, TS_VALUE_COL
 
 import logging
 
@@ -313,8 +313,8 @@ def evaluate_model_CV(
         groups = kf.groups
         kf = kf.split(X_train_split, y_train_split, groups)
         shuffle = False
-    elif isinstance(kf, TimeSeriesSplit) and task == "forecast":
-        y_train_all = pd.DataFrame(y_train_all, columns=["y"])
+    elif isinstance(kf, TimeSeriesSplit) and task == TS_FORECAST:
+        y_train_all = pd.DataFrame(y_train_all, columns=[TS_VALUE_COL])
         train = X_train_all.join(y_train_all)
         kf = kf.split(train)
         shuffle = False