We describe a technique for fast compression of time series, indexing of compressed series, and retrieval of series similar to a given pattern. The compression procedure identifies important points of a series and discards the other points. We use the important points not only for compression, but also for indexing a database of time series. Experiments show the effectiveness of this technique for indexing of stock prices, weather data and electroencephalograms
In many application domains, data can be represented as a series of values (time series). Examples i...
A time series is a sequence of data point which is measured through repeated measurements over unifo...
Data mining and knowledge discovery algorithms for time series data use primitives such as bursts, p...
We describe a procedure for identifying major minima and maxima of a time series, and present two ap...
We describe a technique for fast compression of time series and indexing of compressed series. We ha...
We describe techniques for fast compression of time series and hierarchical indexing of compressed s...
There has been huge progress in the time series domain. Every day, a large volume of time series dat...
The extent of time related data across many fields has led to substantial interest in the analysis o...
The detection of similarities withing the time series provided by the Google \(n\)-gram data can hel...
The need for pattern discovery in long time series data led researchers to develop algorithms for si...
Most pattern mining methods yield a large number of frequent patterns, and isolating a small relevan...
The need for pattern discovery in long time series data led researchers to develop algorithms for si...
Smart objects are increasingly widespread and their ecosystem, also known as the Internet of Things,...
Popularity of time series databases for predicting future events and trends in applications such as ...
Similarity search in time series data is required in many application fields. The most prominent wor...
In many application domains, data can be represented as a series of values (time series). Examples i...
A time series is a sequence of data point which is measured through repeated measurements over unifo...
Data mining and knowledge discovery algorithms for time series data use primitives such as bursts, p...
We describe a procedure for identifying major minima and maxima of a time series, and present two ap...
We describe a technique for fast compression of time series and indexing of compressed series. We ha...
We describe techniques for fast compression of time series and hierarchical indexing of compressed s...
There has been huge progress in the time series domain. Every day, a large volume of time series dat...
The extent of time related data across many fields has led to substantial interest in the analysis o...
The detection of similarities withing the time series provided by the Google \(n\)-gram data can hel...
The need for pattern discovery in long time series data led researchers to develop algorithms for si...
Most pattern mining methods yield a large number of frequent patterns, and isolating a small relevan...
The need for pattern discovery in long time series data led researchers to develop algorithms for si...
Smart objects are increasingly widespread and their ecosystem, also known as the Internet of Things,...
Popularity of time series databases for predicting future events and trends in applications such as ...
Similarity search in time series data is required in many application fields. The most prominent wor...
In many application domains, data can be represented as a series of values (time series). Examples i...
A time series is a sequence of data point which is measured through repeated measurements over unifo...
Data mining and knowledge discovery algorithms for time series data use primitives such as bursts, p...
DOI
Add to ORKG