Online ARIMA Algorithms for Time Series Prediction
Authors: Chenghao Liu, Steven C.H. Hoi, Peilin Zhao, Jianling Sun
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, our encouraging experimental results further validate the effectiveness and robustness of our method. In this section, we conduct experiments on both synthetic and real data to examine the effectiveness and robustness of our online ARIMA algorithms. |
| Researcher Affiliation | Academia | Chenghao Liu1,2, Steven C. H. Hoi2, Peilin Zhao3, Jianling Sun1 1School of Computer Science and Technology, Zhejiang University, China 2School of Information Systems, Singapore Management University, Singapore 3Institute for Infocomm Research, A*STAR, Singapore |
| Pseudocode | Yes | Algorithm 1 ARIMA-ONS(k, d, q) Algorithm 2 ARIMA-OGD(k,d,q) |
| Open Source Code | Yes | All the datasets and source codes for our experiments can be found in our webpage http://OARIMA.stevenhoi.org |
| Open Datasets | Yes | All the datasets and source codes for our experiments can be found in our webpage http://OARIMA.stevenhoi.org |
| Dataset Splits | No | The paper describes using synthetic and real-world datasets but does not provide specific percentages, sample counts, or citations to predefined splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, memory, or specific computing environments used for running the experiments. |
| Software Dependencies | No | The paper does not provide any specific software dependencies with version numbers. |
| Experiment Setup | Yes | To evaluate different algorithms, we design experiments for several settings, in which each experiment was repeated 20 times to yield stable average results and we choose parameter m + k = 10 for all the settings. |