TLDR: TimePD is a new framework for source-free time series forecasting that adapts models to new domains with scarce data without accessing original source data. It uses three key components: invariant disentangled feature learning to handle cross-domain shifts, proxy denoising to calibrate large language model (LLM) biases, and knowledge distillation to align denoised LLM predictions with target model outputs. Experiments show TimePD outperforms state-of-the-art baselines by an average of 9.3% in MAE, demonstrating its effectiveness and generalization ability on real-world datasets.
Time series forecasting, which involves predicting future values based on historical data, is crucial for many real-world applications, from traffic management to energy consumption. With the explosion of data from mobile devices and IoT sensors, the demand for accurate forecasting methods has grown significantly. However, a major challenge arises when a model trained on a large dataset (source domain) needs to be adapted to a new domain where data is scarce (target domain), especially without access to the original source data due to privacy concerns or logistical limitations. This is known as source-free time series forecasting (SF-TSF).
Traditional methods often struggle with sparse target data, leading to a significant drop in performance. Furthermore, existing transfer learning techniques, while useful in other fields like computer vision, don’t always capture the unique temporal correlations inherent in time series data. The emergence of large language models (LLMs) offers a promising avenue due to their powerful generalization capabilities, but they are prone to generating irrelevant or misleading outputs, a phenomenon known as ‘hallucinations,’ particularly when faced with limited domain-specific signals.
Introducing TimePD: A Novel Framework for Source-Free Time Series Forecasting
To address these complex challenges, researchers have developed TimePD, a groundbreaking framework that leverages LLMs and sophisticated feature learning to achieve robust source-free time series forecasting. TimePD is designed to adapt a pre-trained model to a new target domain using only its parameters, without needing the original source data.
The TimePD framework operates through three core components:
1. Invariant Disentangled Feature Learning (IDFL): This module is crucial for tackling the cross-domain distribution shift. It works by explicitly breaking down input time series into their fundamental seasonal and trend components. Imagine separating the yearly pattern of sales (seasonality) from the overall growth or decline over time (trend). IDFL then ensures that the model learns features for these components that remain stable and consistent, regardless of variations between the source and target domains. This is achieved through a dual-branch architecture that enforces invariance at both the representation level (making similar patterns look alike in the feature space) and the gradient level (guiding model updates in a unified direction).
2. Proxy Denoising: While LLMs offer immense potential, their tendency to ‘hallucinate’ can introduce noise into predictions. TimePD’s proxy denoising mechanism dynamically quantifies and corrects these systematic biases. It treats the LLM as a powerful but potentially noisy ‘proxy forecaster.’ By comparing the LLM’s predictions with the consensus between the source model and the adapting target model, TimePD estimates the noise and subtracts it, resulting in more reliable forecasts. This process is parameter-free and adapts as the target model learns.
3. Knowledge Distillation: To enhance efficiency and ensure the LLM’s knowledge is effectively transferred, TimePD employs knowledge distillation. This component creates a bidirectional transfer loop: the denoised predictions from the LLM guide the lightweight target model, while the target model’s predictions, in turn, help stabilize the LLM’s correction process, preventing it from drifting away from the target domain’s specifics. This allows the target model to absorb high-level temporal knowledge from the LLM while maintaining its adaptability to the new domain.
Experimental Validation and Promising Results
Extensive experiments were conducted on six real-world time series datasets, including ETTh1, ETTh2, ETTm1, ETTm2, Weather, Traffic, and Electricity. TimePD was compared against several state-of-the-art baselines in source-free long-term forecasting scenarios. The results were highly encouraging: TimePD consistently achieved superior performance across most datasets and prediction lengths. On average, it reduced the Mean Squared Error (MSE) by 10.7% and the Mean Absolute Error (MAE) by 9.3% compared to existing advanced methods like Time-LLM and OFA.
The framework showed particular effectiveness on complex datasets, such as ETTh1→Weather and ETTh1→Traffic, where baseline methods often struggle with noise and periodicity. An ablation study further confirmed the critical role of each component, with knowledge distillation proving to be the most impactful, followed by proxy denoising and the LLM itself.
Visualizations of data distribution and prediction consistency demonstrated that TimePD’s forecasts closely align with actual values, accurately capturing both periodic trends and sudden changes. Furthermore, invariant feature visualizations confirmed that the model successfully learns the underlying seasonal and trend patterns between source and target data.
Also Read:
- FETA: A New Approach to Training-Free Time Series Classification with LLM Agents
- SPEAR: Enhancing Anomaly Detection in Time Series Data with Soft Prompts and Language Models
Conclusion
TimePD represents a significant step forward in source-free time series forecasting. By combining invariant disentangled feature learning, proxy denoising for LLMs, and knowledge distillation, it effectively addresses the challenges of sparse target data, cross-domain shifts, and LLM hallucinations. This framework not only improves prediction accuracy but also offers a new paradigm for cross-domain time series analytics. The full research paper can be found here.
