Whittle Networks: A Deep Likelihood Model for Time Series
Authors: Zhongjie Yu, Fabrizio G Ventola, Kristian Kersting
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results on stock market data, synthetic time series, MNIST, and hyperspectral images demonstrate that Whittle Networks can indeed capture complex dependencies between time series and provide a useful measure of uncertainty for neural networks. |
| Researcher Affiliation | Academia | Zhongjie Yu 1 Fabrizio Ventola 1 Kristian Kersting 1 2 1Department of Computer Science, TU Darmstadt, Darmstadt, Germany 2Centre for Cognitive Science, TU Darmstadt, and Hessian Center for AI (hessian.AI). Correspondence to: Zhongjie Yu <yu@cs.tu-darmstadt.de>. |
| Pseudocode | No | The paper describes methods and concepts but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source code is available at: https://github.com/ ml-research/Whittle Networks |
| Open Datasets | Yes | Therefore, we use two real-world market datasets acquired from Yahoo! Finance Data . The first one is the index values of 11 sectors from Standard & Poor s (S&P) from October 16, 2013 to May 24, 2019 (See Fig. 1 (Left)). The second one is the global stock index (Stock) from 17 markets extracted from June 2, 1997 to June 30, 1999. Both S&P and Stock datasets are applied first with log-return transformation, assuming them to be stationary (St aric a & Granger, 2005), and then a sliding window of size 32, ending up in 44 and 50 time series instances. |
| Dataset Splits | No | The paper mentions 'training' and 'test' data splits for evaluation (e.g., in Table 1) but does not explicitly specify a 'validation' dataset split for hyperparameter tuning or early stopping. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions various software tools and implementations like 'Learn SPN', 'Res SPNs', 'RAT-SPN', 'MADE', and 'Open Markov toolbox', but it does not specify concrete version numbers for these software dependencies, which would be necessary for full reproducibility. |
| Experiment Setup | Yes | The AE consists of an MLP with the following number of neurons for each layer: 128 64 16 2 16 64 128, using sigmoid as the activation function. and The number of hidden layers in MADE is set to 1 for all datasets, while the hidden units vary from 200 to 600, depending on the number of RVs in each dataset. |