Periodicity detection in time series databases
Periodicity mining is used for predicting trends in time series data. Discovering the rate at which the time series is periodic has always been an obstacle for fully automated periodicity mining. Existing periodicity mining algorithms assume that the periodicity, rate (or simply the period) is user-...
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| Veröffentlicht in: | IEEE transactions on knowledge and data engineering 2005-07, Vol.17 (7), p.875-887 |
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| container_title | IEEE transactions on knowledge and data engineering |
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| creator | Elfeky, M.G. Aref, W.G. Elmagarmid, A.K. |
| description | Periodicity mining is used for predicting trends in time series data. Discovering the rate at which the time series is periodic has always been an obstacle for fully automated periodicity mining. Existing periodicity mining algorithms assume that the periodicity, rate (or simply the period) is user-specified. This assumption is a considerable limitation, especially in time series data where the period is not known a priori. In this paper, we address the problem of detecting the periodicity rate of a time series database. Two types of periodicities are defined, and a scalable, computationally efficient algorithm is proposed for each type. The algorithms perform in O(n log n) time for a time series of length n. Moreover, the proposed algorithms are extended in order to discover the periodic patterns of unknown periods at the same time without affecting the time complexity. Experimental results show that the proposed algorithms are highly accurate with respect to the discovered periodicity rates and periodic patterns. Real-data experiments demonstrate the practicality of the discovered periodic patterns. |
| doi_str_mv | 10.1109/TKDE.2005.114 |
| format | Article |
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Discovering the rate at which the time series is periodic has always been an obstacle for fully automated periodicity mining. Existing periodicity mining algorithms assume that the periodicity, rate (or simply the period) is user-specified. This assumption is a considerable limitation, especially in time series data where the period is not known a priori. In this paper, we address the problem of detecting the periodicity rate of a time series database. Two types of periodicities are defined, and a scalable, computationally efficient algorithm is proposed for each type. The algorithms perform in O(n log n) time for a time series of length n. Moreover, the proposed algorithms are extended in order to discover the periodic patterns of unknown periods at the same time without affecting the time complexity. Experimental results show that the proposed algorithms are highly accurate with respect to the discovered periodicity rates and periodic patterns. 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Discovering the rate at which the time series is periodic has always been an obstacle for fully automated periodicity mining. Existing periodicity mining algorithms assume that the periodicity, rate (or simply the period) is user-specified. This assumption is a considerable limitation, especially in time series data where the period is not known a priori. In this paper, we address the problem of detecting the periodicity rate of a time series database. Two types of periodicities are defined, and a scalable, computationally efficient algorithm is proposed for each type. The algorithms perform in O(n log n) time for a time series of length n. Moreover, the proposed algorithms are extended in order to discover the periodic patterns of unknown periods at the same time without affecting the time complexity. Experimental results show that the proposed algorithms are highly accurate with respect to the discovered periodicity rates and periodic patterns. 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Data processing</subject><subject>Meteorology</subject><subject>Mining</subject><subject>Obstacles</subject><subject>Pattern analysis</subject><subject>Software</subject><subject>Studies</subject><subject>Temperature measurement</subject><subject>temporal data mining</subject><subject>Time series</subject><subject>Time series analysis</subject><subject>time series forecasting</subject><subject>Trends</subject><subject>Weather forecasting</subject><issn>1041-4347</issn><issn>1558-2191</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqF0c9LwzAUB_AiCs4fR09eiqCeOt9L86tHmfMHDvQwzyVNXyBja2fTHfbfm7LBwIOSQ_J4nzwSvklyhTBGhOJh_v40HTMAEUt-lIxQCJ0xLPA4noFjxnOuTpOzEBYAoJXGUTL-pM63tbe-36Y19WR73zapb9LerygNsUshrU1vKhMoXCQnziwDXe738-TreTqfvGazj5e3yeMss5yrPitMRVBriy53tRBUQVE5i5w7JdEKWVtboWEcJOTcOWW4ZSAEEkqVa6vz8-R-N3fdtd8bCn258sHScmkaajeh1IVElUvkUd79KVkhUAGD_6GGuISK8OYXXLSbronfLQtkoBQDGVG2Q7ZrQ-jIlevOr0y3LRHKIY1ySKMc0ojl8Mzb_VATrFm6zjTWh8MlqUXMhEV3vXOeiA5tnjNZ6PwH982QMQ</recordid><startdate>20050701</startdate><enddate>20050701</enddate><creator>Elfeky, M.G.</creator><creator>Aref, W.G.</creator><creator>Elmagarmid, A.K.</creator><general>IEEE</general><general>IEEE Computer Society</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Discovering the rate at which the time series is periodic has always been an obstacle for fully automated periodicity mining. Existing periodicity mining algorithms assume that the periodicity, rate (or simply the period) is user-specified. This assumption is a considerable limitation, especially in time series data where the period is not known a priori. In this paper, we address the problem of detecting the periodicity rate of a time series database. Two types of periodicities are defined, and a scalable, computationally efficient algorithm is proposed for each type. The algorithms perform in O(n log n) time for a time series of length n. Moreover, the proposed algorithms are extended in order to discover the periodic patterns of unknown periods at the same time without affecting the time complexity. Experimental results show that the proposed algorithms are highly accurate with respect to the discovered periodicity rates and periodic patterns. 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| subjects | Algorithms Applied sciences Complexity Computational efficiency Computer science control theory systems Data mining Data processing. List processing. Character string processing Economic forecasting Energy consumption Energy measurement Exact sciences and technology Index Terms- Periodic patterns mining Memory organisation. Data processing Meteorology Mining Obstacles Pattern analysis Software Studies Temperature measurement temporal data mining Time series Time series analysis time series forecasting Trends Weather forecasting |
| title | Periodicity detection in time series databases |
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