Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Second Order Techniques for Learning Time-series with Structural Breaks
Authors: Takayuki Osogami9259-9267
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The effectiveness of the proposed approaches is demonstrated with real time-series. We empirically demonstrate the effectiveness of the proposed techniques with real time-series datasets. We conduct numerical experiments to answer the following questions. |
| Researcher Affiliation | Industry | Takayuki Osogami IBM Research Tokyo EMAIL |
| Pseudocode | Yes | Algorithm 1 Online learning by following the best hyper forgetting rate (single target) |
| Open Source Code | No | The paper does not include any explicit statements about the release of open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We use the 10-year (from September 1, 2008 to August 31, 2018) historical data of the daily close price of Standard & Poor s 500 Stock Index (US index; SPX), Nikkei 225 (Japanese index; Nikkei 225), Deutscher Aktienindex (German index; DAX), Financial Times Stock Exchange 100 Index (UK index; FTSE 100), and Shanghai Stock Exchange Composite Index (Chinese index; SSEC). |
| Dataset Splits | No | The paper describes an online learning setting where models are continuously updated. It states: 'for a time-series of length N, we make a prediction about the next value at every step n for 0 < n < N. When we make a prediction at step n, the time-series up to step n is used to train the models.' This does not constitute a traditional training/validation/test split. |
| Hardware Specification | Yes | We run our experiments on a workstation having eight Intel Core i7-6700K CPUs running at 4.00 GHz and 64 GB random access memory. |
| Software Dependencies | No | The paper does not specify version numbers for any software components or libraries used in the experiments. |
| Experiment Setup | Yes | Input: Nmod = 30, Nhyp = 11; γ1 = λ1 = 0, γi Unif[0.51/D, 1], λi Unif[0, 1], i [2, Nmod]; ηj = 0.89 + 0.01 j, j [1, Nhyp]. We set (µt, at) = ( 10, 0.3) for t < 1, 000, and (µt, at) = (10, 0.3) for t 1, 000. We learn the AR model with the first order with Algorithm 1. |