Results 261 to 270 of about 80,641 (302)
Some of the next articles are maybe not open access.
Time Series Data Mining: A Unifying View
Proceedings of the VLDB Endowment, 2023Time series data are ubiquitous; large volumes of such data are routinely created in scientific, industrial, entertainment, medical and biological domains. Examples include ECG data, gait analysis, stock market quotes, machine health telemetry, search engine throughput volumes etc.
openaire +1 more source
Preserving Privacy in Time Series Data Mining
International Journal of Data Warehousing and Mining, 2011Time series data mining poses new challenges to privacy. Through extensive experiments, the authors find that existing privacy-preserving techniques such as aggregation and adding random noise are insufficient due to privacy attacks such as data flow separation attack.
Ye Zhu 0001 +2 more
openaire +1 more source
Mining and Forecasting of Big Time-series Data
Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, 2015Given a large collection of time series, such as web-click logs, electric medical records and motion capture sensors, how can we efficiently and effectively find typical patterns? How can we statistically summarize all the sequences, and achieve a meaningful segmentation? What are the major tools for forecasting and outlier detection?
Yasushi Sakurai +2 more
openaire +1 more source
Mining Big Time-series Data on the Web
Proceedings of the 25th International Conference Companion on World Wide Web - WWW '16 Companion, 2016Online news, blogs, SNS and many other Web-based services has been attracting considerable interest for business and marketing purposes. Given a large collection of time series, such as web-click logs, online search queries, blog and review entries, how can we efficiently and effectively find typical time-series patterns?
Yasushi Sakurai +2 more
openaire +1 more source
Data Mining in Time Series Databases
, 2004 Adding the time dimension to real-world databases produces Time SeriesDatabases (TSDB) and introduces new aspects and difficulties to datamining and knowledge discovery.
Mark Last, Abraham Kandel, Horst Bunke
openaire +2 more sources
Mining asynchronous periodic patterns in time series data
IEEE Transactions on Knowledge and Data Engineering, 2000Periodicy detection in time series data is a challenging problem of great importance in many applications. Most previous work focused on mining synchronous periodic patterns and did not recognize the misaligned presence of a pattern due to the intervention of random noise. In this paper, we propose a more flexible model of asynchronous periodic pattern
Jiong Yang 0001 +2 more
openaire +1 more source
Introducing time series chains: a new primitive for time series data mining
Knowledge and Information Systems, 2018Time series motifs were introduced in 2002 and have since become a fundamental tool for time series analytics, finding diverse uses in dozens of domains. In this work, we introduce Time Series Chains, which are related to, but distinct from, time series motifs.
Yan Zhu 0014 +3 more
openaire +1 more source
Analyzing time-series data by fuzzy data-mining technique
2005 IEEE International Conference on Granular Computing, 2005Time series analysis has always been an important and interesting research field due to its frequent appearance in different applications. In this paper, we attempt to use the data mining technique to analyze time series. Many previous studies on data mining have focused on handling binary-valued data.
Chun-Hao Chen +2 more
openaire +1 more source
Mining Dense Periodic Patterns in Time Series Data
22nd International Conference on Data Engineering (ICDE'06), 2006Existing techniques to mine periodic patterns in time series data are focused on discovering full-cycle periodic patterns from an entire time series. However, many useful partial periodic patterns are hidden in long and complex time series data. In this paper, we aim to discover the partial periodicity in local segments of the time series data.
Chang Sheng, Wynne Hsu, Mong-Li Lee
openaire +1 more source
Mining and Forecasting of Big Time-Series Data
2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 2019Given a large collection of time series, such as motion capture sensors and automobile trajectories, how can we efficiently and effectively find typical patterns? How can we statistically summarize all the sequences, and achieve a meaningful segmentation? What are the major tools for fore-casting and outlier detection? Time-series data analysis becomes
openaire +1 more source