Coherent Probabilistic Forecasts for Hierarchical Time Series
Authors: Souhaib Ben Taieb, James W. Taylor, Rob J. Hyndman
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the accuracy of our forecasting algorithm on both simulated data and large-scale electricity smart meter data. The results show consistent performance gains compared to state-of-the art methods. |
| Researcher Affiliation | Academia | 1Monash University, Melbourne, Australia 2University of Oxford, Oxford, UK. |
| Pseudocode | Yes | Algorithm 1. (Bottom-up Probabilistic Forecasting) and Algorithm 2. (Mean Combined and Reconciled Probabilistic Forecasting) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, such as a specific repository link or an explicit code release statement. |
| Open Datasets | Yes | We used smart meter electricity consumption data collected by four energy supply companies in Great Britain (AECOM, 2011). |
| Dataset Splits | Yes | We split each time series into training, validation and test sets; the first 12 months for training, the next month for validation and the remaining, approximately, three months for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | For the aggregate series, we capture the yearly cycle, the within-day and within-week seasonalities using seasonal Fourier terms with coefficients estimated by LASSO. After extracting the trend and seasonalities, we fitted an ARIMA model and computed Gaussian predictive distributions. This is justified by the fact that aggregate series are often smoother and easier to forecast, and by the central limit theorem. For the base forecasts, we implemented the kernel density estimation approach that performed the best in the work of Arora & Taylor (2016). |