Second Order Techniques for Learning Time-series with Structural Breaks
Authors: Takayuki Osogami9259-9267
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | 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 osogami@jp.ibm.com |
| 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. |