Towards Better Forecasting by Fusing Near and Distant Future Visions
Authors: Jiezhu Cheng, Kaizhu Huang, Zibin Zheng3593-3600
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | 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. |