Minimax Time Series Prediction
Authors: Wouter M. Koolen, Alan Malek, Peter L. Bartlett, Yasin Abbasi Yadkori
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we study the time series prediction problem in the regret framework; instead of making assumptions on the data generating process, we ask: can we predict the data sequence online almost as well as the best offline prediction method in some comparison class (in this case, offline means that the comparator only needs to model the data sequence after seeing all of it)? Our main contribution is computing the exact minimax strategy for a range of time series prediction problems. As a concrete motivating example, let us pose the simplest nontrivial such minimax problem |
| Researcher Affiliation | Academia | Wouter M. Koolen Centrum Wiskunde & Informatica wmkoolen@cwi.nl Alan Malek UC Berkeley malek@berkeley.edu Peter L. Bartlett UC Berkeley & QUT bartlett@cs.berkeley.edu Yasin Abbasi-Yadkori Queensland University of Technology yasin.abbasiyadkori@qut.edu.au |
| Pseudocode | No | The paper describes algorithms and recurrences (e.g., in Section 4 and 5) but does not include formal pseudocode blocks or algorithms labeled as such. |
| Open Source Code | No | The paper does not mention releasing code or provide any links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not use or mention any specific dataset for training or provide access information for a dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation or provide dataset split information. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers for implementation or experiments. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |