python - Normalize Pandas Column with skewed values

Question

I have a pandas dataframe univariable time series data. The values of the column range between -1 and 1. However, it is left skewed with some outlier values greater than 0.5.

I want to do time series forecasting on this dataset but the predict result has been poor irrespective of the parameter tuning. I believe one issue might be with raw data - although it is between -1 and 1, the outliers might be making it difficult to predict (note these 'outliers' are valid occurrences of the event).

My question: is there a way to normalise the data such that the extreme values will not be pronounced? I tried log-normalization but wouldn't work with negative values and other scaling methods tend to keep the distribution.

question from:https://stackoverflow.com/questions/65841490/normalize-pandas-column-with-skewed-values

Answer 1

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