Capturing the dynamical properties of time series concisely as interpretable feature vectors
can enable efficient clustering and classification for time-series applications across science …

A recent experimental evaluation assessed 19 time series classification (TSC) algorithms
and found that one was significantly more accurate than all others: the Flat Collective of …

Cardiovascular diseases affect approximately 50 million people worldwide; thus, heart
disease prevention is one of the most important tasks of any health care system. Despite the …

Recently, two ideas have been explored that lead to more accurate algorithms for time-
series classification (TSC). First, it has been shown that the simplest way to gain …

Similarity search is one of the most important and probably best studied methods for data
mining. In the context of time series analysis it reaches its limits when it comes to mining raw …

Several alternative distance measures for comparing time series have recently been
proposed and evaluated on time series classification (TSC) problems. These include …

Abstract Time Series Classification (TSC) has seen enormous progress over the last two
decades. HIVE-COTE (Hierarchical Vote Collective of Transformation-based Ensembles) is …

Time-series classification (TSC) problems present a specific challenge for classification
algorithms: how to measure similarity between series. A shapelet is a time-series …

Time series (TS) occur in many scientific and commercial applications, ranging from earth
surveillance to industry automation to the smart grids. An important type of TS analysis is …

The problem of time series classification (TSC), where we consider any real-valued ordered
data a time series, presents a specific machine learning challenge as the ordering of …