Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network
Authors: Lifeng Shen, Zhuocong Li, James Kwok
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive empirical studies on various real-world timeseries demonstrate that the proposed THOC network outperforms recent strong deep learning baselines on timeseries anomaly detection. Experiments performed on a number of real-world timeseries data sets show that the proposed model outperforms the recent state-of-the-arts. |
| Researcher Affiliation | Collaboration | Lifeng Shen1, Zhuocong Li2, James T. Kwok1 1 Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong {lshenae,jamesk}@cse.ust.hk 2 Cloud and Smart Industries Group, Tencent, China zhuocongli@tencent.com |
| Pseudocode | Yes | Algorithm 1 Temporal hierarchical one-class learning (THOC). Input: timeseries Xs = (x1,s, x2,s, . . . , x Ts,s); number of centers {Kl}; skip lengths {s(l)}. 1: repeat 2: feed xt,s into the L-layer dilated-RNN and obtain {f l t} from each layer; 3: for layer l = 1, . . . , L do 4: obtain the lth clustering layer s input { f l 1 t,i }i=1,...,Kl 1 by (8) where K0 =1; 5: compute probabilities {P l t,i j}i=1,...,Kl 1,j=1,...,Kl from (5); 6: compute {Rl t,j}j=1,...,Kl for each cluster center at layer l from (10); 7: update and obtain output features {ˆf l t,j}j=1,...,Kl from (7); 8: end for 9: minimize MVDD objective in (13) by the Adam optimizer; 10: until convergence. |
| Open Source Code | No | The paper states 'Source codes of the baselines are downloaded from the web.' but does not provide a statement or link for the source code of their own proposed method. |
| Open Datasets | Yes | The following timeseries data sets are used: (i) 2D-gesture [9], which records the X-Y coordinate sequences of hand gestures in a video; (ii) Power demand[9], which contains a year of power demand at a Dutch research facility; (iii) KDD-Cup99 data from the DARPA 98 Intrusion Detection Evaluation Program [14]. It contains around seven million network traffic connection records over a 7-week period. (iv) Secure Water Treatment (SWa T) data [18], which is collected from a water treatment testbed over 11 days. (v) Mars Science Laboratory rover (MSL); and (vi) Soil Moisture Active Passive satellite (SMAP) data: Both MSL and SMAP are public data sets from NASA [8]. |
| Dataset Splits | Yes | For 2D-gesture, power-demand, KDD-Cup99, and SWa T, the raw data set has only a training set and a test set. To allow model selection and hyperparameter tuning, we use part of the provided test set for validation. For MSL and SMAP, we follow the setting in [28], and hold out 30% of the training data as validation set. A summary of the resultant data set statistics is shown in Table 1. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with versions). |
| Experiment Setup | No | The paper states 'Detailed experimental settings can be found in Appendix B.', implying that specific setup details like hyperparameters are not present in the main text provided. |