Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network
Authors: Lifeng Shen, Zhuocong Li, James Kwok
NeurIPS 2020 | Venue PDF | 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 EMAIL 2 Cloud and Smart Industries Group, Tencent, China EMAIL |
| 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 {หf 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 traf๏ฌc 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. |