A Decomposition Approach for Urban Anomaly Detection Across Spatiotemporal Data
Authors: Mingyang Zhang, Tong Li, Hongzhi Shi, Yong Li, Pan Hui
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method using both real-world and synthetic datasets. The results show our method can detect meaningful events and outperforms state-of-the-art anomaly detecting methods by a large margin. |
| Researcher Affiliation | Academia | 1The Hong Kong University of Science and Technology 2Tsinghua University 3University of Helsinki |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | The first dataset is the NYC taxi trip records and the second one is the NYC bike trip records1... 1http://www.nyc.gov/html/tlc/html/about/trip record data.shtml. Besides, we crawled the weather data of New York during the data collection period from Wunder Ground2. 2https://www.wunderground.com |
| Dataset Splits | No | The paper explicitly mentions a train/test split for the synthetic dataset ('We use the data of the first 19 weeks for training and the data of the last week for testing.') but does not specify a separate validation split or explicit cross-validation setup. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using a 'neural network' but does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Refer to Figure 3, we set both FC network I and II as twolayer networks and FC network III as a three-layer network. λ in (5) is set to 10 3 in the experiments. ... We set λ = {0, 10 4, 10 3, 10 2, 10 1, 1} and run our model on the synthetic dataset. |