Basic Examples (4) 

Create an example time series:

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Visualize the path:

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Four redundant points are removed from the time series by compression:

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Visualize the removed points:

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Scope (3) 

Compress a list of numeric values:

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Compress a list of time-value pairs:

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Compressing TemporalData applies compression to all underlying paths. The resampling is changed to linear interpolation:

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Options (4) 

Increasing the maximal allowed deviation in the time series value leads to better compression:

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Visualize the removed points:

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Backtesting the compressed time series values with the original time series values:

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Limit the maximal time distance between two points:

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Possible Issues (3) 

TimeSeriesCompress only works on time series whose values are scalars. Time series with higher-dimensional values are returned uncompressed:

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EventSeries is a special case of TemporalData allowing no interpolation. To compress an EventSeries, convert it to a TimeSeries:

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TimeSeriesCompress removes values with head Missing:

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Neat Examples (3) 

Use TemporalData to store the stock prices of the FAANG companies since the beginning of the decade:

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If you are not interested in cent fluctuations of the prices of these stocks, you can work with a compressed representation:

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Compression reduces the required data points by about 40 percent:

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