DTW Example - Teradata Vantage

This example compares multiple time series to both a common template and each other. Each time series represents stock prices and the template represents a series of stock index prices.

Input

SQL Call

SELECT * FROM DTW (
  ON timeseriesdata AS input_table
    PARTITION BY timeseriesid
    ORDER BY timestamp1
  ON templatedata AS template_table DIMENSION
    ORDER BY timestamp2
  ON mappingdata AS mapping_table
    PARTITION BY timeseriesid
  USING
  TargetColumns ('stockprice', 'timestamp1')
  TemplateColumns ('indexprice', 'timestamp2')
  TimeSeriesID ('timeseriesid')
  TemplateID ('templateid')
) AS dt ORDER BY "timeseries_id";

Output

Plot and Interpretation of Results

The warping distance is an unnormalized measure of how dissimilar two time series are. The warp_distance column in the output table has the warping distance for all pairs in the mapping table; that is, for every timeseries_id and template_id number.

Because the dissimilarity of two time series is not based on whether they are temporarily close (the time is stretched and the two time series that are offset by a constant time interval are effectively the same), input 3 is not very dissimilar to templates 1 and 3. However, input 4 has the largest warping distance measure from templates 1 and 3, as the curvature of the latter 2 is far from input 4. Time stretching brings input 4 closer to templates 1 and 3, but with a larger warping path (not output above) and therefore, a larger warping distance.