In the last 5 years there have been a large number of new time series classification
algorithms proposed in the literature. These algorithms have been evaluated on subsets of …

Clustering is a solution for classifying enormous data when there is not any early knowledge
about classes. With emerging new concepts like cloud computing and big data and their vast …

Traffic flow prediction plays an important role in ITS (Intelligent Transportation System). This
task is challenging due to the complex spatial and temporal correlations (eg, the constraints …

Time series classification is an increasing research topic due to the vast amount of time
series data that is being created over a wide variety of fields. The particularity of the data …

The proliferation and ubiquity of temporal data across many disciplines has generated
substantial interest in the analysis and mining of time series. Clustering is one of the most …

China's commercial Bank shares have become the backbone of the capital market. The
prediction of a bank's stock price has been a hot topic in the investment field. However, the …

This paper presents a review of the literature on network traffic prediction, while also serving
as a tutorial to the topic. We examine works based on autoregressive moving average …

Most clustering strategies have not changed considerably since their initial definition. The
common improvements are either related to the distance measure used to assess …

This paper presents the first time series clustering benchmark utilizing all time series
datasets currently available in the University of California Riverside (UCR) archive—the …

The all-relevant problem of feature selection is the identification of all strongly and weakly
relevant attributes. This problem is especially hard to solve for time series classification and …