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..
Conditional Information Bottleneck-Based Multivariate Time Series Forecasting
Authors: Xinhui Li, Liang Duan, Lixing Yu, Kun Yue, Yuehua Li
IJCAI 2025 | Venue PDF | 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. |