Learning Temporal Causal Sequence Relationships from Real-Time Time-Series
Authors: Antonio Anastasio Bruto da Costa, Pallab Dasgupta
JAIR 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The application of the proposed methodology is illustrated through various examples. [...] In Section 8 we demonstrate the utility of the methodology through select case studies. |
| Researcher Affiliation | Academia | Antonio Anastasio Bruto da Costa EMAIL Pallab Dasgupta EMAIL Dept. of Computer Science Indian Institute of Technology Kharagpur Kharagpur, West Bengal, India 721302 |
| Pseudocode | Yes | ALGORITHM 1: nk-PSI-Miner: Mining n-length, k-resolution Prefix Sequences |
| Open Source Code | Yes | The theory developed in this article has been implemented in a tool called the Prefix Sequence Inference Miner (PSI-Miner), available at https://github.com/antoniobruto/PSIMiner. |
| Open Datasets | No | The paper describes using custom or simulation data for its case studies (e.g., "position information from multiple vehicles in Town-X", "simulation of an LDO circuit", "100 passengers"), but does not provide concrete access information such as links, DOIs, or specific citations for publicly available datasets. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., training/testing/validation percentages or counts). It describes the total amount of data used (e.g., "nine vehicles", "100 passengers") but not how it was partitioned for experimentation. |
| Hardware Specification | Yes | The miner was used on a standard laptop with a 2.40GHz Intel Core i7-5500U CPU with 8GB of RAM. |
| Software Dependencies | No | The paper mentions its tool, PSI-Miner, but does not list any specific software dependencies or library versions (e.g., Python, PyTorch, scikit-learn, etc.) that would be needed to replicate the experiments. |
| Experiment Setup | Yes | For each example, we choose meta-parameters n, the number of intervals in the antecedent, and k, the initial time delay between buckets. [...] A delay-resolution of k = 2min and a maximum sequence length of n = 15 are used in the experiments. [...] We use a sequence length of n = 5. On average, the time to move between way-points is known to be 70mins. We use a time delay of 70mins between events in the sequence. [...] We use a low support threshold (10 4%). |