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..
Learning Temporal Causal Sequence Relationships from Real-Time Time-Series
Authors: Antonio Anastasio Bruto da Costa, Pallab Dasgupta
JAIR 2021 | Venue PDF | 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%). |