图 书 馆
分类时间序列的Granger因果网络Granger Causality Networks for Categorical Time Series |
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| 课程网址: | https://videolectures.net/videos/kdd2016_tank_granger_causality |
| 主讲教师: | Alex Tank |
| 开课单位: | KDD 2016研讨会 |
| 开课时间: | 2025-02-04 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We present two model-based methods for learning Granger causality networks for multivariate categorical time series. Our first proposal is based on the mixture transition distribution (MTD) model. Traditionally, MTD is plagued by a nonconvex objective, non-identifiability, and presence of many local optima. To circumvent these problems, we recast inference in the MTD as a convex problem. The new formulation facilitates the application of MTD to high-dimensional multivariate time series. Our second proposal is based on a multi-output logistic autoregressive model, which while a straightforward extension, has not been previously applied to the analysis of multivariate categorial time series. We investigate identifiability conditions of both methods, devise novel optimization algorithms for the MTD, and compare the MTD and mLTD in simulated experiments. Our approach simultaneously provides a comparison of methods for network inference in categorical time series and opens the door to modern, regularized inference in MTD model. |
| 关 键 词: | 分类时间序列; 因果网络; 基于模型 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2025-03-31：liyq |
| 最后编审: | 2025-03-31：liyq |
| 阅读次数: | 1 |