Predict with Time series regression

Hello,

I'm training a Time series regression model and I have a problem when predicting.


This error appears because I'm not passing the target on the `y` parameter.
But how I can pass the target for a future value?

I also tried modifying your predict method with the next code:

```python3
import pandas as pd

from evalml.pipelines.pipeline_meta import TimeSeriesPipelineBaseMeta
from evalml.pipelines.regression_pipeline import RegressionPipeline
from evalml.problem_types import ProblemTypes
from evalml.utils import (
_convert_woodwork_types_wrapper,
drop_rows_with_nans,
infer_feature_types,
pad_with_nans
)
from types import MethodType

def predict(self, X, y=None, objective=None):
"""Make predictions using selected features.
Arguments:
X (ww.DataTable, pd.DataFrame, or np.ndarray): Data of shape [n_samples, n_features]
y (ww.DataColumn, pd.Series, np.ndarray, None): The target training targets of length [n_samples]
objective (Object or string): The objective to use to make predictions
Returns:
ww.DataColumn: Predicted values.
"""
if X is None:
X = pd.DataFrame()
X = infer_feature_types(X)
X = _convert_woodwork_types_wrapper(X.to_dataframe())
if y is not None:
y = infer_feature_types(y)
y = _convert_woodwork_types_wrapper(y.to_series())
features = self.compute_estimator_features(X, y)
features = _convert_woodwork_types_wrapper(features.to_dataframe())
features_no_nan, y = drop_rows_with_nans(features, y)
y_arg = None
if self.estimator.predict_uses_y:
y_arg = y
predictions = self.estimator.predict(features_no_nan, y_arg).to_series()
predictions = predictions.rename(self.input_target_name)
padded = pad_with_nans(predictions, max(0, features.shape[0] - predictions.shape[0]))
return infer_feature_types(padded)

best_pipeline.predict = MethodType(predict, best_pipeline)
```

And then the error is the next one:

Do you have any idea what I am doing wrong?
How can a predict for future values?

Thank you very much