Spectral Subsampling MCMC for Stationary Time Series
Authors: Robert Salomone, Matias Quiroz, Robert Kohn, Mattias Villani, Minh-Ngoc Tran
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We consider several time series models for large data in our experiments, including the recently proposed class of autoregressive tempered fractionally integrated moving average (ARTFIMA) models and several of its widely-used special cases such as ARMA and ARFIMA though we highlight that any model class for which the spectral density is known can be used. |
| Researcher Affiliation | Academia | 1UNSW Sydney 2University of Technology Sydney 3Stockholm University 4Link oping University 5University of Sydney. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about making the source code available or links to a code repository for the methodology described. |
| Open Datasets | Yes | Example 1: Vancouver Temperatures (ARMA) sourced from openweathermap.org. Example 2: Stockholm Temperatures (ARTFIMA) obtained from the Swedish Meteorological and Hydrological Institute (www.smhi.se/en). Example 4: Bitcoin Prices Stochastic Volatility (ARTFIMA-SV) from coinbase.com. |
| Dataset Splits | No | The paper mentions dataset lengths (e.g., 'length n = 44001') but does not specify training, validation, or test splits by percentage or sample count. No explicit split methodology is provided. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions 'CODA package (Plummer et al., 2006) in R' and 'auto.arima function in the Forecast (Hyndman & Khandakar, 2008) package in R', but does not provide specific version numbers for R, CODA, or Forecast, which are required for reproducibility. |
| Experiment Setup | Yes | Table 1. Settings for models ARMA(2,3) (M1), ARTFIMA(2,2) (M2), ARFIMA(2,1) (M3) and ARTFIMA-SV(1,1) (M4). The table shows number of frequency observations (n), number of groups (|G|), number of observations per group (|G|, the same for all groups), percentage of subsampled groups (m) and number of blocks in the block pseudo-marginal algorithm (B). The coreset settings are number of iterations of GIGA algorithm (M), number of random projections (RP) (see Campbell & Broderick (2018) for details) and the average size of the coreset (gk). |