Sequential Monte Carlo Learning for Time Series Structure Discovery
Authors: Feras Saad, Brian Patton, Matthew Douglas Hoffman, Rif A. Saurous, Vikash Mansinghka
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical measurements on real-world time series show that our method can deliver 10x 100x runtime speedups over previous MCMC and greedy-search structure learning algorithms targeting the same model family. We use our method to perform the first large-scale evaluation of Gaussian process time series structure learning on a prominent benchmark of 1,428 econometric datasets. |
| Researcher Affiliation | Collaboration | Feras A. Saad 1 2 Brian J. Patton 2 Matthew D. Hoffmann 2 Rif A. Saurous 2 Vikash K. Mansinghka 3 2 1Carnegie Mellon University 2Google Research 3Massachusetts Institute of Technology. |
| Pseudocode | Yes | Algorithm 1 SMC Structure Learning via Data Annealing |
| Open Source Code | Yes | Available online at https://github.com/fsaad/Auto GP.jl |
| Open Datasets | Yes | We further evaluate our method on 1,428 monthly datasets from M3 (Makridakis & Hibon, 2000) |
| Dataset Splits | No | The paper uses the M3 dataset for forecasting, which implies a training and test split based on time horizons ('18 forecast horizons'). However, it does not explicitly state numerical percentages or sample counts for training, validation, and test sets, nor does it cite the specific predefined split methodology for M3 in the main text. |
| Hardware Specification | Yes | All the experiments were conducted on a Google Cloud n2d-standard-48 instance (server specs: AMD EPYC 7B12 48v CPU @2.25GHz, 192 GB RAM). |
| Software Dependencies | No | The paper mentions 'Gen probabilistic programming system' and 'statsforecast and neuralforecast packages', but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | For Auto GP, Algorithm 1 was run using a linear annealing schedule with 5% of the data introduced at each step; M = 48 particles; adaptive resampling with ESS = M/2 = 24; and Nrejuv = 100 MCMC rejuvenation steps. The training time points t and (demeaned) values y are linearly transformed to [0, 1]. |