Sk7w4tch3r/ts_similarity_tensorflow

Three (dis)similarity measures for time series implemented in Tensorflow

Similarity measures for time series implemented in tensorflow.

1. Soft-Dynamic Time Warping (SDTW).

• File: SoftDTW.py
• Notes: Inputs of shape: (n,t,f).
  • n: number of multivariate time series.
  • t: length of time series.
  • f: number of features.
• Source: Cuturi, Blondel (2017) - Soft-DTW: a Differentiable Loss Function for Time-Series

2. Complexity-Invariant Distance (CID).

• File: CIDTF.py
• Notes: Inputs of shape: (n,t,1)...currently implemented for univariate time series only. Multivariate implementation might follow.
  • n: number of multivariate time series.
  • t: length of time series.
• Source: Batista et al. (2011) - A Complexity-Invariant Distance Measure for Time Series

3. Euclidean distance (ED).

• File: EDTF.py
• Notes: Inputs of shape: (n,t,f).
  • n: number of multivariate time series.
  • t: length of time series.
  • f: number of features.
• Borrowed from: Guo et al. (2017) - Improved Deep Embedded Clustering with Local Structure Preservation