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 [1].
Influence-Driven Model for Time Series Prediction from Partial Observations
Authors: Saima Aman, Charalampos Chelmis, Viktor Prasanna
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated our approach with a large real-world electricity consumption data collected from smart meters in Los Angeles and the results show that between prediction horizons of 2 to 8 hours, despite lack of real time data, our influence model outperforms the baseline model that uses real-time data. |
| Researcher Affiliation | Academia | Saima Aman Department of Computer Science University of Southern California Los Angeles, California Email: EMAIL Charalampos Chelmis and Viktor K. Prasanna Department of Electrical Engineering University of Southern California Los Angeles, California Email: EMAIL |
| Pseudocode | No | The paper describes the methodology using text and mathematical formulations but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | 1) Electricity Consumption Data2: collected at 15-min intervals by over 170 smart meters installed in the USC campus microgrid (Simmhan et al. 2013) in Los Angeles. 2) Weather Data: temperature and humidity data taken from NOAA s (NOAA 2013) USC campus station, linearly interpolated to 15-min resolution. |
| Dataset Splits | No | The paper mentions 'training data from a previous similar day' and compares two choices ('previous week' and 'previous day') for training. However, it does not specify explicit percentages or counts for training, validation, and test splits typically used for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud instances) used to run the experiments. |
| Software Dependencies | No | The paper mentions using regression trees and specific models like ART, but it does not list any specific software packages, libraries, or their version numbers used in the implementation. |
| Experiment Setup | Yes | Given the short horizon, the length of previous values used was set to 1-hour. For each sensor si, we sort the corresponding row M[i, ] in the dependency matrix and consider only readings from the top τl sensors in this model. (τ = 4, 8, 12, 16, 20). After sorting the sensors based on their influence values, we consider only readings from the top τg sensors in the influence model. (τ = 4, 8, 12, 16, 20). |