Similarity Measure of Time Series With Different Sampling Frequencies Based on Context Density Consistency and Dynamic Time Warping

Similarity measure of time series with different sampling frequencies is vitally important for many signal processing applications. Dynamic Time Warping (DTW) is one of the most popular methods for similarity measure of time series. However, conventional DTW algorithms have limitations when dealing with time series of different sampling frequencies due to context density inconsistency such as different internal change frequencies of derivatives, shapes, events and distances in local neighborhoods. In light of this, we propose a novel Context Density Consistency Dynamic Time Warping (CDC-DTW) algorithm. It firstly designs local context windows adaptive to the lengths of time series. Then it proposed a local spatial-temporal context density consistency technique by down-sampling and interpolation compensating the high-frequency time series following the context density of low-frequency time series. Besides, a normalized Hamming window weighting function is embedded into the local contexts to create robust weighted cost measure. Extensive experimental results on 128 gold-standard UCR datasets showed that CDC-DTW increased the similarity measure accuracy by 70.53% in average comparing with other 6 classic and state-of-the-art DTW baseline algorithms.

Automated summary

The CDC-DTW algorithm proposed in this study is a method for measuring the similarity of time series with different sampling frequencies. By designing adaptive local context windows and using the technique of local spatial-temporal context density consistency, it overcomes the limitations of conventional DTW algorithms and achieves high accuracy in similarity measure, as demonstrated in extensive experimental results on 128 gold-standard UCR datasets.