Time-series bug when running tutorials on FLAML==2.1.0

^{Originally posted by **LeonardoEssence** September 21, 2023}
I was running some of the AutoML examples on the documentation [here](https://microsoft.github.io/FLAML/docs/Examples/AutoML-Time%20series%20forecast#univariate-time-series), and the code for all time series examples kept breaking at a pandas `key error` prompt. See below:

`
Traceback (most recent call last):
File "/mnt/uni_variate_time_series_flaml.py", line 30, in
automl.fit(dataframe=train_df, # training data
File "/opt/conda/lib/python3.9/site-packages/flaml/automl/automl.py", line 1663, in fit
task.validate_data(
File "/opt/conda/lib/python3.9/site-packages/flaml/automl/task/time_series_task.py", line 167, in validate_data
data = TimeSeriesDataset(
File "/opt/conda/lib/python3.9/site-packages/flaml/automl/time_series/ts_data.py", line 57, in __init__
self.frequency = pd.infer_freq(train_data[time_col].unique())
File "/opt/conda/lib/python3.9/site-packages/pandas/core/frame.py", line 3505, in __getitem__
indexer = self.columns.get_loc(key)
File "/opt/conda/lib/python3.9/site-packages/pandas/core/indexes/base.py", line 3623, in get_loc
raise KeyError(key) from err
KeyError: 'index'
`

I went deep into the code and found what I believe is a small bug in the class `TimeSeriesTask`, when calling the function `TimeSeriesDataset` in line 167 in the file **time_series_task.py**.

The function is expecting a data frame with train data **and** the time stamp vector, however, the code in line 165, is only concatenating `Xt` and `yt`, leaving out the time vector.

I propose to change line 165 from `df_t = pd.concat([Xt, yt], axis=1)` to `df_t = pd.concat([pre_data.all_data[pre_data.time_col], Xt, yt], axis=1)`. That worked for me, however, I'm not 100% sure that's the intended functionality but as it is now, it is not working.

Is anybody finding the same? or can provide some suggestions?