Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning
Jia Liu, Cheng Jinguo, Xia Fang, Zhenyuan Ma, Yuankai Wu·April 20, 2025
Summary
Foundational time series models, including Time-MoE and Chronos, show improved predictive accuracy through incremental learning, outperforming traditional models. This study introduces new evaluation methods for developing robust, continuously learning models. Challenges in real-world data shifts are addressed, emphasizing the need for dynamic model adaptation. Three studies explore time series analysis using large language models, focusing on general analysis, forecasting, and autoregressive forecasting. Continual learning techniques, such as maintaining plasticity and overcoming catastrophic forgetting, are discussed, along with the effectiveness of transformers in time series analysis.
Background
Overview of foundational time series models
Time-MoE and Chronos
Improved predictive accuracy through incremental learning
Outperformance of traditional models
Challenges in real-world data shifts
Need for dynamic model adaptation
Method
New evaluation methods for robust, continuously learning models
Framework for assessing incremental learning capabilities
Metrics for evaluating model adaptability
Addressing real-world data shifts
Techniques for handling concept drift
Strategies for maintaining model performance over time
Studies on Time Series Analysis with Large Language Models
General Analysis
Utilization of large language models for time series understanding
Comparative analysis with traditional models
Forecasting
Application of large language models for predictive tasks
Evaluation of forecasting accuracy and reliability
Autoregressive Forecasting
Implementation of autoregressive models using large language models
Analysis of model performance in sequential prediction
Continual Learning Techniques
Maintaining Model Plasticity
Strategies for keeping models adaptable to new data
Techniques for preventing overfitting to historical data
Overcoming Catastrophic Forgetting
Methods for preserving knowledge from previous learning tasks
Approaches to mitigate the loss of information when learning new tasks
Effectiveness of Transformers in Time Series Analysis
Utilization of transformer architectures for time series tasks
Comparison with traditional time series models
Advantages in handling sequential data and long-term dependencies
Conclusion
Summary of findings
Future directions
Ongoing research challenges
Potential applications and improvements
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
What innovative approaches are introduced to address real-world data shifts in time series analysis?
What are the challenges and limitations identified in using transformers for time series analysis?
What are the main contributions of the study regarding time series models like Time-MoE and Chronos?
How do the new evaluation methods improve the development of robust, continuously learning models?