Efficient State-Space Inference of Periodic Latent Force Models
Authors: Steven Reece, Siddhartha Ghosh, Alex Rogers, Stephen Roberts, Nicholas R. Jennings
JMLR 2014 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our approach to model the thermal dynamics of domestic buildings and show that it is effective at predicting day-ahead temperatures within the homes. We also apply our approach within queueing theory in which quasi-periodic arrival rates are modelled as latent forces. In both cases, we demonstrate that our approach can be implemented efficiently using state-space methods which encode the linear dynamic systems via LFMs. Further, we show that state estimates obtained using periodic latent force models can reduce the root mean squared error to 17% of that from non-periodic models and 27% of the nearest rival approach which is the resonator model (S arkk a et al., 2012; Hartikainen et al., 2012). |
| Researcher Affiliation | Academia | Steven Reece EMAIL Department of Engineering Science University of Oxford Parks Road Oxford OX1 3PJ, UK Siddhartha Ghosh EMAIL Alex Rogers EMAIL Electronics and Computer Science University of Southampton Southampton SO17 1BJ, UK Stephen Roberts EMAIL Department of Engineering Science University of Oxford Parks Road Oxford OX1 3PJ, UK Nicholas R. Jennings EMAIL Electronics and Computer Science University of Southampton Southampton SO17 1BJ, UK and Department of Computing and Information Technology King Abdulaziz University Saudi Arabia |
| Pseudocode | No | The paper describes methods and equations for state-space inference, Kalman filters, and RBPF, but it does not include any explicitly labeled pseudocode or algorithm blocks with structured steps. |
| Open Source Code | No | The paper mentions code from a third-party (L azaro-Gredilla et al. (2010)) as being available online, but there is no statement or link indicating that the authors of this paper have released their own source code for the methodology described. |
| Open Datasets | Yes | We use real customer arrival rate data, provided by Feigin et al. (2006), in which the customer telephone arrival rates for a loan company sales line have been collected every 5 minutes over a three month period starting from October 2001. |
| Dataset Splits | Yes | The efficacy of our customer queue model is evaluated by training the model using data over three full consecutive Thursdays and then tracking the mean queue length over the following Thursday. The queue length observations are made every 40 minutes during the training period and every three hours during the fourth day tracking phase. |
| Hardware Specification | Yes | The run times were determined using a Macbook Pro with a 2.4 GHz Intel Core i7 processor and 8 GB of memory. |
| Software Dependencies | No | The paper mentions methods like the Nelder-Mead optimisation algorithm and the Kalman filter, but it does not specify any software libraries or tools with version numbers used for implementation. |
| Experiment Setup | No | The paper describes the setup of the experiments, including the number of days for training and tracking, and general parameters like the fixed cycle period of 24 hours. However, it does not explicitly provide specific hyperparameter values or detailed system-level training settings like learning rates, batch sizes, or optimizer configurations in the main text. |