Optimal Algorithms for the Online Time Series Search Problem

2009 ◽  
pp. 322-333 ◽  
Author(s):  
Yinfeng Xu ◽  
Wenming Zhang ◽  
Feifeng Zheng
Keyword(s):  
Time Series ◽  
Search Problem ◽  
Optimal Algorithms ◽  
Time Series Search

scholarly journals Optimal algorithms for the online time series search problem

2011 ◽  
Vol 412 (3) ◽  
pp. 192-197 ◽  
Author(s):  
Yinfeng Xu ◽  
Wenming Zhang ◽  
Feifeng Zheng
Keyword(s):  
Time Series ◽  
Search Problem ◽  
Optimal Algorithms ◽  
Time Series Search

Online algorithms for the multiple time series search problem

2012 ◽  
Vol 39 (5) ◽  
pp. 929-938 ◽  
Author(s):  
Wenming Zhang ◽  
Yinfeng Xu ◽  
Feifeng Zheng ◽  
Yucheng Dong
Keyword(s):  
Time Series ◽  
Online Algorithms ◽  
Search Problem ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Time Series Search

Optimal algorithms for online time series search and one-way trading with interrelated prices

2010 ◽  
Vol 23 (2) ◽  
pp. 159-166 ◽  
Author(s):  
Wenming Zhang ◽  
Yinfeng Xu ◽  
Feifeng Zheng ◽  
Yucheng Dong
Keyword(s):  
Time Series ◽  
Optimal Algorithms ◽  
Time Series Search

scholarly journals Hybrid Algorithm for Anomaly Removal in Time Series Data Mining

2021 ◽  
Author(s):  
Abdul Razaque ◽  
Marzhan Abenova ◽  
Munif Alotaibi ◽  
Bandar Alotaibi ◽  
Hamoud Alshammari ◽  
...  
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Hybrid Algorithm ◽  
Time Series Data ◽  
State Of The Art ◽  
Large Data ◽  
Series Data ◽  
Multidimensional Data ◽  
Search Problem ◽  
Short Text

Time series data are significant and are derived from temporal data, which involve real numbers representing values collected regularly over time. Time series have a great impact on many types of data. However, time series have anomalies. We introduce hybrid algorithm named novel matrix profile (NMP) to solve the all-pairs similarity search problem for time series data. The proposed NMP inherits the features from two state-of-the art algorithms: similarity time-series automatic multivariate prediction (STAMP), and short text online microblogging protocol (STOMP). The proposed algorithm caches the output in an easy-to-access fashion for single- and multidimensional data. The proposed NMP algorithm can be used on large data sets and generates approximate solutions of high quality in a reasonable time. The proposed NMP can also handle several data mining tasks. It is implemented on a Python platform. To determine its effectiveness, it is compared with the state-of-the-art matrix profile algorithms i.e., STAMP and STOMP. The results confirm that the proposed NMP provides higher accuracy than the compared algorithms.

Time Series Analysis

2014 ◽  
Author(s):  
Andrew J. Connolly ◽  
Jacob T. VanderPlas ◽  
Alexander Gray ◽  
Andrew J. Connolly ◽  
Jacob T. VanderPlas ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Applied Mathematics ◽  
Sampled Data ◽  
Series Analysis ◽  
Time Domain Data ◽  
Noise Data ◽  
Time Series Search ◽  
Modeling Toolkit

This chapter summarizes the fundamental concepts and tools for analyzing time series data. Time series analysis is a branch of applied mathematics developed mostly in the fields of signal processing and statistics. Contributions to this field, from an astronomical perspective, have predominantly focused on unevenly sampled data, low signal-to-noise data, and heteroscedastic errors. The chapter starts with a brief introduction to the main concepts in time series analysis. It then discusses the main tools from the modeling toolkit for time series analysis. Despite being set in the context of time series, many tools and results are readily applicable in other domains, and for this reason the examples presented will not be strictly limited to time-domain data. Armed with the modeling toolkit, the chapter goes on to discuss the analysis of periodic time series, search for temporally localized signals, and concludes with a brief discussion of stochastic processes.

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