Similarity Search on Time Series Based on Threshold Queries

Abstract

Abstract. Similarity search in time series data is required in many application fields. The most prominent work has focused on similarity search considering either complete time series or similarity according to subsequences of time series. For many domains like financial analysis, medicine, environmental meteorology, or environmental observation, the detection of temporal dependencies between different time series is very important. In contrast to traditional approaches which consider the course of the time series for the purpose of matching, coarse trend information about the time series could be sufficient to solve the above mentioned problem. In particular, temporal dependencies in time series can be detected by determining the points of time at which the time series exceeds a specific threshold. In this paper, we introduce the novel concept of threshold queries in time series databases which report those time series exceeding a user-defined query threshold at similar time frames compared to the query time series. We present a new efficient access method which uses the fact that only partial information of the time series is required at query time. The performance of our solution is demonstrated by an extensive experimental evaluation on real world and artificial time series data.

Periodic Pattern Analysis in Time Series Databases

“…As proposed in [3,4], time series considering a single time domain can be represented as a sequence of intervals according to a certain threshold value τ . For the recognition of relevant periodic patterns that are hidden in the matrix representation of dual-domain time series, we extend this approach to a novel abstract meaning.…”

“…Considering the single-domain representation of time series, we performed similarity queries on the given datasets utilizing the techniques that are applicable for computing similarity on single-domain time series, such as the Euclidean distance (in the following denoted as EUCL), the DTW [7] and the threshold-based approach [3,4], in the following referred to as THR. Later in this section, we outline the obtained results of our newly introduced approach of measuring the distances for comparison.…”

“…Here, we take special emphasis on events that refer to an exceeding of a given activity threshold. Similarity search methods based on events that refer to exceeding of a given activity threshold have been introduced in [3,4]. Given a certain threshold value τ , this approach reduces singledomain time series to a sequence of intervals corresponding to time periods where the amplitude value of a time series exceeds τ .…”

Textual Approximation Methods for Time Series Classification: TAX and l-TAX

“…Such a system takes a query time series as its input and finds the most similar time series from the database [1]- [6]. These methods apply time series dimension reduction techniques to transform the time series into its features in a feature space using certain transformation functions.…”

T-Time: Threshold-Based Data Mining on Time Series

“…Recently, we proposed the new concept of defining similarity between time series based on thresholds [2], [3]. Threshold similarity considers intervals during which the time series exceeds a certain threshold for comparing time series rather than using the exact time series values.…”