How do I assess whether two time series change together?

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In this example, at timepoint 5, both signals move up together. I would like to quantify these similar movements, and ideally disregard the parts where the signals are almost constant. What correlation or similarity measures would be best here?

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Tirtha

Posted 2019-10-30T09:58:45.193

Reputation: 23

Answers

1

One option is a Granger causality test which a statistical hypothesis test for determining whether one time series is useful in forecasting another. One time series is said to Granger-cause another time series if the lagged values of one times series provide statistically significant information about future values of another time series.

Brian Spiering

Posted 2019-10-30T09:58:45.193

Reputation: 10 864

0

This looks like a job for Dynamic Time Warping (DTW), as this algorithm calculates an optimal match between two given sequences. If you would like to implement it in Python (for example), I can recommend DTAIDistance. The documentation of this project is also very helpful if you want to understand this method in detail.

Hagbard

Posted 2019-10-30T09:58:45.193

Reputation: 394

But DTW would also count the constant parts as similar, wouldn't it? I would ideally like to disregard or at least give less weight to the parts where one or both are constant. – Tirtha – 2019-10-30T12:17:46.360