| Presentation | 2015-12-04 Method for Detecting Explicit Structural Changes in Time Series Data Akira Kasuga, Yukio Ohsawa, Takaaki Yoshino, Shunichi Ashida, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In recent years, Anomaly Detection is noticed in order to prevent a risk, perform security system and analyze behaviors. It is common to define the anomaly values using the probabilistic distribution estimation wherein the latent variable is assumed in Anomaly Detection. However, the data we can obtain in business are often heterogeneous and deficient. If the exiting methods are applied to heterogeneous and deficient data, it is difficult to analyze these data accurately and make a decision because the latent variable models result in complicated. In this paper, we propose the method that can detect explicit structural changes from high dimensional data of time series with the aim of detecting changes without assuming the latent variables. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Change-Point Detection / Time Series / Explicit Change / Chance Discovery / Affinity Propagation |
| Paper # | AI2015-21 |
| Date of Issue | 2015-11-27 (AI) |
| Conference Information | |
| Committee | AI |
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| Conference Date | 2015/12/4(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Kyutech-Salite |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Toshiharu Sugawara(Waseda Univ.) |
| Vice Chair | Tsunenori Mine(Kyushu Univ.) / Daisuke Katagami(Tokyo Polytechnic Univ.) |
| Secretary | Tsunenori Mine(Ritsumeikan Univ.) / Daisuke Katagami(Shizuoka Univ.) |
| Assistant | Yuichi Sei(Univ. of Electro-Comm.) |
| Paper Information | |
| Registration To | Technical Committee on Artificial Intelligence and Knowledge-Based Processing |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Method for Detecting Explicit Structural Changes in Time Series Data |
| Sub Title (in English) | |
| Keyword(1) | Change-Point Detection |
| Keyword(2) | Time Series |
| Keyword(3) | Explicit Change |
| Keyword(4) | Chance Discovery |
| Keyword(5) | Affinity Propagation |
| 1st Author's Name | Akira Kasuga |
| 1st Author's Affiliation | University of Tokyo(UTokyo) |
| 2nd Author's Name | Yukio Ohsawa |
| 2nd Author's Affiliation | University of Tokyo(UTokyo) |
| 3rd Author's Name | Takaaki Yoshino |
| 3rd Author's Affiliation | Daiwa Securities Co. Ltd.(Daiwa Securities) |
| 4th Author's Name | Shunichi Ashida |
| 4th Author's Affiliation | Daiwa Securities Co. Ltd.(Daiwa Securities) |
| Date | 2015-12-04 |
| Paper # | AI2015-21 |
| Volume (vol) | vol.115 |
| Number (no) | AI-337 |
| Page | pp.pp.51-55(AI), |
| #Pages | 5 |
| Date of Issue | 2015-11-27 (AI) |