Conditional Information Bottleneck-Based Multivariate Time Series Forecasting
Authors: Xinhui Li, Liang Duan, Lixing Yu, Kun Yue, Yuehua Li
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical investigations on authentic datasets underscore the superiority of our proposed approach over other cuttingedge competitors. Our code is available at https: //github.com/Xinhui-Lee/CIB-MTSF. We conduct extensive experiments on 9 real-world datasets, as outlined in [Huang et al., 2024], including ETT datasets (ETTh1, ETTh2, ETTm1, ETTm2), Weather, Traffic, Electricity, ILI, and Exchange Rate. In all experiments, we adopt the same train/val/test split ratio of 6:2:2 for ETT datasets and 7:1:2 for others. We adopt MSE and mean absolute error (MAE) to evaluate the effectiveness of our method. |
| Researcher Affiliation | Academia | Xinhui Li 1,2 , Liang Duan 1,2 , Lixing Yu 1,2 , Kun Yue 1,2 and Yuehua Li 3 1 Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, Kunming, China 2 School of Information Science and Engineering, Yunnan University, Kunming, China 3 School of Earth Science, Yunnan University, Kunming, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 CIB-based MTS forecasting Input: X: historical MTS data Parameters: P: length of patches, T: total training epochs, lr: learning rate, ϕ: parameters of encoder, θ: parameters of variational bound, ξ: parameters of forecasting head Output: ˆX: forecasting results 1: Divide X into M overlapped patches Xp 2: Initialize ϕ, θ, and ξ 3: for t = 1 to T do 4: Z qϕ(Z|XP ) 5: Calculate Lk by Eq. (14) // Constraint on encoder 6: Generate samples Z+ i and Z i 7: Calculate Lc by Eq. (9) // Inter-series correlations 8: Ls 0 9: for k = 3 to M do 10: Calculate Iθ(Zik; Zik 1|Zik 2, . . . , Zi1) 11: Ls Ls + Iθ(Zik; Zik 1|Zik 2, . . . , Zi1) 12: end for 13: Ls Ls/(M 2) // Constraint on time coherences 14: ˆX fξ(Z) // Generating forecasting results 15: Calculate Lm by Eq. (20) // MSE loss 16: L Lm + α1Lk + α1Lc α2Ls 17: ϕ ϕ lr L // Updating parameters 18: θ θ lr L 19: ξ ξ lr L 20: end for 21: return ˆX |
| Open Source Code | Yes | Our code is available at https: //github.com/Xinhui-Lee/CIB-MTSF. |
| Open Datasets | Yes | We conduct extensive experiments on 9 real-world datasets, as outlined in [Huang et al., 2024], including ETT datasets (ETTh1, ETTh2, ETTm1, ETTm2), Weather, Traffic, Electricity, ILI, and Exchange Rate. |
| Dataset Splits | Yes | In all experiments, we adopt the same train/val/test split ratio of 6:2:2 for ETT datasets and 7:1:2 for others. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions parameters like 'P: length of patches, T: total training epochs, lr: learning rate' and hyperparameters 'α1' and 'α2' in Algorithm 1 and Section 4.4, but does not provide their concrete values in the main text. The only specific setting for experiments is 'the input sequence length L is set to 96 for Exchange, 60 for ILI, and 336 for others' which are dataset characteristics, not model hyperparameters. |