توضیحات
ABSTRACT
In almost every scientific field, measurements are performed over time. These observations lead to a collection of organized data called time series. The purpose of time-series data mining is to try to extract all meaningful knowledge from the shape of data. Even if humans have a natural capacity to perform these tasks, it remains a complex problem for computers. In this article we intend to provide a survey of the techniques applied for time-series data mining. The first part is devoted to an overview of the tasks that have captured most of the interest of researchers. Considering that in most cases, time-series task relies on the same components for implementation, we divide the literature depending on these common aspects, namely representation techniques, distance measures, and indexing methods. The study of the relevant literature has been categorized for each individual aspects. Four types of robustness could then be formalized and any kind of distance could then be classified. Finally, the study submits various research trends and avenues that can be explored in the near future. We hope that this article can provide a broad and deep understanding of the time-series data mining research field.
INTRODUCTION
A time series represents a collection of values obtained from sequential measurements over time. Time-series data mining stems from the desire to reify our natural ability to visualize the shape of data. Humans rely on complex schemes in order to perform such tasks. We can actually avoid focusing on small fluctuations in order to derive a notion of shape and identify almost instantly similarities between patterns on various time scales. Major time-series-related tasks include query by content [Faloutsos et al. 1994], anomaly detection [Weiss 2004], motif discovery [Lin et al. 2004], prediction [Weigend and Gershenfeld 1994], clustering [Lin and Keogh 2005], classification [Bakshi and Stephanopoulos 1994], and segmentation [Keogh et al. 2003a]. Despite the vast body of work devoted to this topic in the early years, Antunes and Oliveira [2001] noted that “the research has not been driven so much by actual problems but by an interest in proposing new approaches”. However, with the ever- growing maturity of time-series data mining techniques, this statement seems to have become obsolete. Nowadays, time-series analysis covers a wide range of real-life problems in various fields of research. Some examples include economic forecasting [Song and Li 2008], intrusion detection [Zhong et al. 2007], gene expression analysis [Lin et al. 2008], medical surveillance [Burkom et al. 2007], and hydrology [Ouyang et al. 2010].
Year: 2011
Publishe: IRCAM
By: CARLOS AGON
File Information: English Language/ 34 Page / size:401KB
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سال : 2011
ناشر : IRCAM
کاری از : CARLOS AGON
اطلاعات فایل : زبان انگلیسی / 34 صفحه / حجم : 401KB
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