#Time series clustering how to#

You could also plot the percentiles of the raw time series data by cluster. You could also plot the percentiles of time series shape by cluster by computing the Fourier series from the amplitudes and phase angles (the resulting time series estimate will not perfectly match the original time series).

#Time series clustering how to#

See this StackOverflow answer on how to do that for real-valued data. You will also want to calculate the amplitudes and phase angles from the fast Fourier transform so that you can explore the distribution of time series spectra within clusters. Plot the percentiles of the time series by cluster to examine their shape.Īlternately, you could omit the DC components of the fast Fourier transform to avoid your clusters being based on the mean and instead on the series defined by the Fourier transform, which represents the shape of the time series.

Perform model-based clustering on the real and imaginary parts of each frequency element. Separate the real and imaginary parts of each fast Fourier transform element. If time series is real-valued, discard the second half of the fast Fourier transform elements because they are redundant. This decomposes your time series data into mean and frequency components and allows you to use variables for clustering that do not show heavy autocorrelation like many raw time series.

Perform a fast Fourier transform on the time series data.