A review on time series data mining

Fu, Tak-chung

doi:10.1016/j.engappai.2010.09.007

Cited by 1,221 publications

(605 citation statements)

References 215 publications

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“…DTW is one of the most favored choices for time-series comparison in the literature, as shown in many existing works [24] [25]. It is a method already utilized in various applications such as speech recognition, automated signature recognition and shape matching.…”

Section: Student's Test and Dtwmentioning

confidence: 99%

Characterizing and predicting mobile application usage

Lim

Secci

Tabourier

et al. 2016

Computer Communications

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In this paper, we propose data clustering techniques to predict temporal characteristics of data consumption behavior of different mobile applications via wireless communications. While most of the research on mobile data analytics focuses on the analysis of call data records and mobility traces, our analysis concentrates on mobile application usages, to characterize them and predict their behavior. We exploit mobile application usage logs provided by a Wi-Fi local area network service provider to characterize temporal behavior of mobile applications. More specifically, we generate daily profiles of "what" types of mobile applications users access and "when" users access them. From these profiles, we create usage classes of mobile applications via aggregation of similar profiles depending on data consumption rate, using three clustering techniques that we compare. Furthermore, we show that we can utilize these classes to analyze and predict future usages of each mobile application through progressive comparison using distance and similarity comparison techniques. Finally, we also detect and exploit outlying behavior in application usage profiles and discuss methods to efficiently predict them.

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Section: Student's Test and Dtwmentioning

confidence: 99%

Characterizing and predicting mobile application usage

Lim

Secci

Tabourier

et al. 2016

Computer Communications

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“…The latter include discretisation methods and string representations applied in this paper (see for example Lin et al 2003), although their application for prediction of FTS in particular and for the purpose of tactical asset allocation is explored to a much lesser extent. A concise and informative overview of time series data mining techniques can be found in Fu (2011). Therefore, we focus less on the alphabet construction method and pay more attention to the way patterns are presented to an algorithm, namely we introduce a new type of string kernel aimed specifically at FTS that are known for their time-conditional characteristics.…”

Section: Previous Workmentioning

confidence: 99%

Discrete representation strategies for foreign exchange prediction

žličar

Cousins

2017

J Intell Inf Syst

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This is an extended version of the paper presented at the 4th International Workshop NFMCP 2015 held in conjunction with ECML PKDD 2015. The initial version has been published in NFMCP 2015 conference proceedings as part of Springer Series. This paper presents a novel approach to financial times series (FTS) prediction by mapping hourly foreign exchange data to string representations and deriving simple trading strategies from them. To measure the degree of similarity in these market strings we apply familiar string kernels, bag of words and n-grams, whilst also introducing a new kernel, time-decay n-grams, that captures the temporal nature of FTS. In the process we propose a sequential Parzen windows algorithm based on discrete representations where trading decisions for each string are learned in an online manner and are thus subject to temporal fluctuations. We evaluate the strength of a number of representations using both the string version and its continuous counterpart, whilst also comparing the performance of different learning algorithms on these representations, namely support vector machines, Parzen windows and Fisher discriminant analysis. Our extensive experiments show that the simple string representation coupled with the sequential Parzen windows approach is capable of outperforming other more exotic approaches, supporting the idea that when it comes to working in high noise environments often the simplest approach is the most effective.

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“…Fu (2011) reviewed data mining applications to time series. Wang et al (2014) used grey models for prediction related to the largest video site in China.…”

Section: Journal Risk Management Applicationsmentioning

confidence: 99%