[timeseries] Automatically convert timestamps to datetime & handle DataFrame inputs

[shchur]

Description of changes:

This PR contains several potential improvements to the data loading process in autogluon.timeseries:

1. Automatically convert the timestamp columns to the datetime64 format when constructing a TimeSeriesDataFrame from pd.DataFrame without multiindex.
2. Load a TimeSeriesDataFrame from path (local or remote), similar to autogluon.tabular.TabularDataset.
3. Accept pd.DataFrame as input to TimeSeriesPredictor and automatically convert to TimeSeriesDataFrame under the hood.

These changes make it possible to significantly shorten the code for training AGTS on a dataset.

• Current minimal example

import pandas as pd
from autogluon.timeseries import TimeSeriesPredictor, TimeSeriesDataFrame

raw_df = pd.read_csv(
    "https://autogluon.s3.amazonaws.com/datasets/timeseries/dummy/dummy.csv", 
    parse_dates=["timestamp"],
)
data = TimeSeriesDataFrame(raw_df)
predictor = TimeSeriesPredictor().fit(data)

• Using Option 2 from this PR

from autogluon.timeseries import TimeSeriesPredictor, TimeSeriesDataFrame

data = TimeSeriesDataFrame("https://autogluon.s3.amazonaws.com/datasets/timeseries/dummy/dummy.csv")
predictor = TimeSeriesPredictor().fit(data)

• Using Option 3 from this PR

import pandas as pd
from autogluon.timeseries import TimeSeriesPredictor

data = pd.read_csv("https://autogluon.s3.amazonaws.com/datasets/timeseries/dummy/dummy.csv")
predictor = TimeSeriesPredictor().fit(data)

By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

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[canerturkmen]

LGTM!