Time Series Similarity search with aeon#

The goal of Time Series Similarity search is to asses the similarities between a time series, denoted as a query q of length l, and a collection of time series, denoted as X, which lengths are superior or equal to l. In this context, the notion of similiarity between q and the other series in X is quantified by similarity functions. Those functions are most of the time defined as distance function, such as the Euclidean distance. Knowing the similarity between q and other admissible candidates, we can then perform many other tasks for “free”, such as anomaly or motif detection.

time series similarity search

Similarity search Notebooks#

This notebook gives an overview of similarity search module and the available estimators. The following notebooks are avaiable to go more in depth with specific subject of similarity search in aeon:

Expected inputs and format#

Available estimators#

All estimators of the similarity search module in aeon inherit from the BaseSimilaritySearch class, which requires the following arguments: - distance : a string indicating which distance function to use as similarity function. By default this is "euclidean", which means that the Euclidean distance is used. - normalize : a boolean indicating whether this similarity function should be z-normalized. This means that the scale of the two series being compared will be ignored, and that, loosely speaking, we will only focus on their shape during the comparison. By default, this parameter is set False.

Another parameter, which has no effect on the output of the estimators, is a boolean named store_distance_profile, set to False by default. If set to True, the estimators will expose an attribute named _distance_profile after the predict function is called. This attribute will contain the computed distance profile for query given as input to the predict function.

To illustrate how to work with similarity search estimators in aeon, we will now present some example use cases.

Generated using nbsphinx. The Jupyter notebook can be found here.