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..
Towards Better Forecasting by Fusing Near and Distant Future Visions
Authors: Jiezhu Cheng, Kaizhu Huang, Zibin Zheng3593-3600
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three real-world datasets show that our method achieves statistically significant improvements compared to the most state-of-the-art baseline methods, with average 4.59% reduction on RMSE metric and average 6.87% reduction on MAE metric. |
| Researcher Affiliation | Collaboration | 1Sun Yat-sen University, School of Data and Computer Science, Guangzhou, China 2Sun Yat-sen University, National Engineering Research Center of Digital Life, Guangzhou, China 3Xi an Jiaotong-Liverpool University, Department of Electrical and Electronic Engineering, Suzhou, China 4Alibaba-Zhejiang University Joint Institute of Frontier Technologies, Hangzhou, China |
| Pseudocode | No | The paper describes the model architecture using mathematical equations and textual descriptions but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | All the data and experiment codes of our model are available at Github1. 1https://github.com/smallGum/MLCNN-Multivariate-Time-Series |
| Open Datasets | Yes | As depicted in Table 1, our experiments are based on three publicly available datasets: Traffic (Lai et al. 2017): This dataset consists of 48 months (2015-2016) hourly data from the California Department of Transportation... Energy (Candanedo, Feldheim, and Deramaix 2017): This UCI appliances energy dataset contains measurements of 29 different quantities... NASDAQ (Qin et al. 2017): This dataset includes the stock prices of 81 major corporations and the index value of NASDAQ 100... |
| Dataset Splits | Yes | Table 1: Dataset statistics ... Train size 60% 80% 90% Valid size 20% 10% 5% Test size 20% 10% 5% |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as CPU or GPU models, or cloud computing specifications. |
| Software Dependencies | No | The paper describes the use of CNN, LSTM, and Adam optimizer but does not specify version numbers for any software libraries, frameworks, or programming languages used in the implementation. |
| Experiment Setup | Yes | For RNN-LSTM, we vary the number of hidden state size in {10, 25, 50, 100, 200}. For MTCNN, the filter number of CNN is chosen from {5, 10, 25, 50, 100}... The dropout rate of our model is chosen from {0.2, 0.3, 0.5}. During the training phase, the batch size is 128 and the learning rate is 0.001. |