Improve performance of __getitem__ of TimeSeriesDataSet

[denix56]

Description

Pandas DataFrame is quite slow in comparison to numpy due to additional checks.
By replacing it with np.recarray I was able to improve performance by 5-10%.
Recarray allows us to have nice attribute access as in pandas, while improving performance.
The raw numpy arrays are a bit faster than recarray, however the difference is not as big as between pandas and recarray.
I have tested on Demand Forecasting with gpu=1, 0 workers and pin_memory=True.

[codecov-commenter]

Codecov Report

Merging #806 (eb706f9) into master (0b5892a) will increase coverage by 0.00%.
The diff coverage is 100.00%.

@@           Coverage Diff           @@
##           master     #806   +/-   ##
=======================================
  Coverage   89.05%   89.06%           
=======================================
  Files          24       24           
  Lines        3829     3832    +3     
=======================================
+ Hits         3410     3413    +3     
  Misses        419      419           

Flag	Coverage Δ
cpu	89.06% <100.00%> (+<0.01%)	⬆️
pytest	89.06% <100.00%> (+<0.01%)	⬆️

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Impacted Files	Coverage Δ
pytorch_forecasting/data/timeseries.py	93.12% <100.00%> (+0.02%)	⬆️

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[jdb78]

I am tempted to merge this. Think we should run the example notebooks also because things might change there - even if only visual.