{"title":"社会影响非线性时间序列分析","authors":"Thinh Minh Do, Yasuko Matsubara, Yasushi Sakurai","doi":"10.1145/2926693.2929902","DOIUrl":null,"url":null,"abstract":"In this paper, we present Δ-SPOT, a non-linear model for analysing large scale web search data, and its fitting algorithm. Δ-SPOT can forecast long-range future dynamics of the keywords/queries. We use the Google Search, Twitter and MemeTracker data set for extensive experiments, which show that our method outperforms other non-linear mining methods. We also provide an online algorithm contributing to the need of monitoring multiple co-evolving data sequences.","PeriodicalId":123723,"journal":{"name":"Proceedings of the 2016 on SIGMOD'16 PhD Symposium","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Non-linear Time-series Analysis of Social Influence\",\"authors\":\"Thinh Minh Do, Yasuko Matsubara, Yasushi Sakurai\",\"doi\":\"10.1145/2926693.2929902\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present Δ-SPOT, a non-linear model for analysing large scale web search data, and its fitting algorithm. Δ-SPOT can forecast long-range future dynamics of the keywords/queries. We use the Google Search, Twitter and MemeTracker data set for extensive experiments, which show that our method outperforms other non-linear mining methods. We also provide an online algorithm contributing to the need of monitoring multiple co-evolving data sequences.\",\"PeriodicalId\":123723,\"journal\":{\"name\":\"Proceedings of the 2016 on SIGMOD'16 PhD Symposium\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-06-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2016 on SIGMOD'16 PhD Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2926693.2929902\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2016 on SIGMOD'16 PhD Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2926693.2929902","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Non-linear Time-series Analysis of Social Influence
In this paper, we present Δ-SPOT, a non-linear model for analysing large scale web search data, and its fitting algorithm. Δ-SPOT can forecast long-range future dynamics of the keywords/queries. We use the Google Search, Twitter and MemeTracker data set for extensive experiments, which show that our method outperforms other non-linear mining methods. We also provide an online algorithm contributing to the need of monitoring multiple co-evolving data sequences.
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