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Many ML models perform better when the input is normalised like stay between 0..1 interval.

How the stock time series with exponential like growth could be represented?

  • It could be normalised into 0..1 interval - but the tail will be almost like 0.

  • It also could be represented as a series of diffs, like Price[n+1] = diff[n]*Price[n], but it has another problem - it's not robust to missing data, if you miss even single diff - it would cause big error going forward. Given that many ML technics involve introducing random noise or losses for better learning - such fragile representation would make it harder.

  • It's also possible to represent it as a logarithmic scale.

I wonder what are the other ways to normalise such data as an input for ML like Neural Nets and others?

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