Can Dynamic Time Warping be used on time series with missing values?

Question

As indicated in the title, I am wondering if the DTW (Dynamic Time Warping) could be used to calculate the DTW distance between two time series with missing values. Let's say the two time series are daily temperatures of two weather stations, and are of equal lengths (e.g. 365 days), and the missing values are on different days for the two time series. If this is possible, is the dtw package in R able to handle the missing values? I didn't find a parameter that could be set in dtw() like `na.rm = T`. Thanks a lot! Thanks thelatemail for the suggestion. Below is a simplified example of the two time series, where each time series contain only 52 elements and the missing values are set to `NA`. ``` TS1 = c(-3.26433, -5.09096, NA, -8.4158, -5.85485, -3.49234, -7.64666, -4.90124, NA, -4.68836, -1.38114, 1.55527, 2.81872, 2.44261, 3.57963, 6.19983, 7.42515, 8.41524, 6.32686, 10.0144, 9.53251, 13.4781, 12.3585, 10.6706, 10.2647, 16.6848, 16.4855, 20.1482, NA, 21.5734, 20.3946, 20.8824, 18.03…

Accepted Answer

Probably not, I looked over the package manual and there is nothing about the missing or NA values. I also tried to feed your data to `dtw()` and it fails:

```
Error in dtw(TS1, TS2) : 
  No warping paths exists that is allowed by costraints
```

But when I changed all NA values to 0, it worked easily.

So if your only solution is this package, you can make a post on the DTW package forum, or probably you have to deal the missing data yourself. You may find some hints here or use the `na()` function of the `fSeries` package*.

*This package is no longer available. It is suggested to use the `timeSeries` package instead.

Can Dynamic Time Warping be used on time series with missing values?

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