tidsseriemotiv

A tidsseriemotiv, often translated as time series motif, is a subsequence within a time series that occurs frequently or is representative of a recurring pattern. Identifying these motifs is a key task in time series data mining, aiming to find segments that exhibit similar behavior over time. The concept is analogous to finding repeating patterns in text, but applied to sequences of numerical data.

The significance of time series motifs lies in their ability to highlight dominant or characteristic behaviors in the data. For example, in sensor data, a motif might represent a typical operating cycle or a common malfunction. In financial time series, a motif could signify a recurring market trend.

Algorithms for finding tidsseriemotivs typically involve comparing all possible subsequences against each other. A common approach is to use distance measures, such as Euclidean distance or dynamic time warping (DTW), to quantify the similarity between subsequences. Subsequences that are close to many other subsequences, or are closest to a consensus pattern, are identified as motifs.

The challenges in motif discovery include the computational complexity of comparing all possible subsequences, especially for long time series. Efficient algorithms often employ techniques like indexing, discretization, or dimensionality reduction to speed up the search. The definition of a "motif" can also vary, sometimes focusing on the most frequent subsequence, while other times seeking the subsequence that is closest to the average behavior of all subsequences. The identified motifs can then be used for various applications, including pattern recognition, anomaly detection, and time series summarization.