Subsequence search and distance measures are crucial tools in time series data mining. This paper presents our Python package entitled TSSEARCH, which provides a comprehensive set of methods for subsequence search and similarity measurement in time series. These methods are user-customizable for more flexibility and efficient integration into real deployment scenarios. TSSEARCH enables fast exploratory time series data analysis and was validated in the context of human activity recognition and indoor localization.publishersversionpublishe
Time series data occurs in many real world applications. For examplea system might have a database w...
The chapter is organized as follows. Section 2 will introduce the similarity matching problem on tim...
We present an efficient indexing method to locate subsequences within a collection of sequences, suc...
Because time series are a ubiquitous and increasingly prevalent type of data, there has been much re...
There has been huge progress in the time series domain. Every day, a large volume of time series dat...
This paper proposes a general framework for matching sim-ilar subsequences in both time series and s...
A method for approximate subsequence matching is introduced, that significantly improves the efficie...
A Time Series Clique (TSC) consists of multiple time series which are related to each other by natur...
In many application domains, data can be represented as a series of values (time series). Examples i...
The last decade has seen a flurry of research on all-pairs-similarity-search (or, self-join) for tex...
As advances in science and technology have continually increased the existence of, and capability fo...
Geolocated time series, i.e., time series associated with certain locations, abound in many modern a...
Similarity search in time series data is required in many application fields. The most prominent wor...
We propose an embedding-based framework for subsequence matching in time-series databases that impro...
Insights from database research, notably in the areas of data mining and similarity search, and adva...
Time series data occurs in many real world applications. For examplea system might have a database w...
The chapter is organized as follows. Section 2 will introduce the similarity matching problem on tim...
We present an efficient indexing method to locate subsequences within a collection of sequences, suc...
Because time series are a ubiquitous and increasingly prevalent type of data, there has been much re...
There has been huge progress in the time series domain. Every day, a large volume of time series dat...
This paper proposes a general framework for matching sim-ilar subsequences in both time series and s...
A method for approximate subsequence matching is introduced, that significantly improves the efficie...
A Time Series Clique (TSC) consists of multiple time series which are related to each other by natur...
In many application domains, data can be represented as a series of values (time series). Examples i...
The last decade has seen a flurry of research on all-pairs-similarity-search (or, self-join) for tex...
As advances in science and technology have continually increased the existence of, and capability fo...
Geolocated time series, i.e., time series associated with certain locations, abound in many modern a...
Similarity search in time series data is required in many application fields. The most prominent wor...
We propose an embedding-based framework for subsequence matching in time-series databases that impro...
Insights from database research, notably in the areas of data mining and similarity search, and adva...
Time series data occurs in many real world applications. For examplea system might have a database w...
The chapter is organized as follows. Section 2 will introduce the similarity matching problem on tim...
We present an efficient indexing method to locate subsequences within a collection of sequences, suc...