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LangTime: A Language-Guided Unified Model for Time Series Forecasting with Proximal Policy Optimization
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Proposes LangTime, an LLM-based unified time series forecaster (Qwen2-0.5B backbone) using language-guided temporal comprehension prompts for cross-domain pre-training, plus TimePPO, a PPO-style RL fine-tuning method to reduce autoregressive error accumulation.
Problem
Applying pre-trained LLMs to time series forecasting faces cross-domain generalization, cross-modality alignment, and error accumulation in autoregressive multi-step forecasting.
Approach
Language-guided Temporal Comprehension Prompts carry dataset and channel descriptions and compress a series into one token for reconstruction and prediction; TimePPO adapts PPO to continuous forecasting with a sequence-level repeat-based value estimate and a multi-dimensional (MSE, MAE, KL) reward.
Key results
- LangTime pre-trained across datasets achieves best results in 58 of 70 entries in the cross-dataset comparison table.
- TimePPO-fine-tuned LangTime surpasses per-dataset trained models in 45 of 70 entries, e.g. Electricity avg MSE 0.201 vs 0.193 for TimesNet (TimesNet best there).
- On Illness zero-shot, LangTime pre-trained averages MSE 4.023 and MAE 1.474 versus UniTime 4.359/1.524 and AutoTimes 4.898/1.587.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| fine-tune LangTime with TimePPO instead of pre-training alone | LangTime · ETTh1 (average over horizons) | MSE: 0.440 -> 0.435 | Helped |
| fine-tune LangTime with TimePPO instead of SFT | LangTime · Weather (average over horizons) | MSE: 0.263 -> 0.252 | Helped |
| add channel information to TCPs (ablation) | LangTime · ETTh1 and Weather | MSE: 0.440 -> 0.436; 0.269 -> 0.267 | Helped |
| add dataset information to TCPs (ablation) | LangTime · ETTh1 and Weather | MSE: 0.442 -> 0.436; 0.272 -> 0.267 | Helped |
| add timestamp information to TCPs (ablation) | LangTime · ETTh1 and Weather | MSE | No clear effect |
| include the MSE reward dimension (ablation) | TimePPO · ETTh1 | MSE: 0.435 -> 0.429 | Helped |
| include the MAE reward dimension (ablation) | TimePPO · ETTh1 | MSE: 0.433 -> 0.429 | Helped |
| include the KL reward dimension (ablation) | TimePPO · ETTh1 | MSE: 0.435 -> 0.429 | Helped |
| replace the Qwen backbone with GPT-2 (ablation) | LangTime · ETTh1 | MSE: 0.448 -> 0.492 | Hurt |
| replace the Qwen backbone with GPT-2 (ablation) | LangTime · Weather | MSE: 0.270 -> 0.275 | No clear effect |
| replace the Qwen backbone with a linear layer (ablation) | LangTime · ETTh1 and Weather | MSE: 0.448 -> 0.723; 0.270 -> 0.301 | Hurt |
| rephrase task instructions without changing their meaning (ablation) | LangTime TCPs · ETTh1 and Weather | MSE: 0.448 -> 0.449; 0.270 -> 0.273 | No clear effect |
Surprising findings
- TimePPO fine-tuning improves Electricity avg MSE, but TimesNet remains best on Electricity MSE average in the per-dataset comparison, so gains are not uniform across datasets.
Limitations
- Channel-independent design; some datasets were split into channel subsets to manage batch size.
- Gains from TimePPO are modest in several settings and sensitive to hyperparameters such as tau on Weather.
- Evaluation limited to standard long-term forecasting benchmarks with fixed look-back 96.
Experimental setup
Datasets: ETTh1 ETTh2 ETTm1 ETTm2 Electricity Exchange Weather Illness Traffic
Benchmarks: ETTh1 ETTh2 ETTm1 ETTm2 Electricity Exchange Weather Illness Traffic
Metrics: MSE MAE
Baselines: UniTime AutoTimes S$^2$IP-LLM Time-LLM GPT4TS PatchTST TimesNet TimesFM
Builds on: proximal policy optimization generalized advantage estimation InstructGPT AutoTimes UniTime
Closest papers by meaning
- Time-LLM: Time Series Forecasting by Reprogramming Large Language Models (ICLR 2024 · similarity 0.90)
- AutoTimes: Autoregressive Time Series Forecasters via Large Language Models (NeurIPS 2024 · similarity 0.90)
- Rethinking Post-Training Recipes for Multimodal Time-Series Forecasting (NeurIPS 2026 · similarity 0.89)
- TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment (AAAI 2025 · similarity 0.89)
- Towards a General Time Series Forecasting Model with Unified Representation and Adaptive Transfer (ICML 2025 · similarity 0.89)
- CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning (AAAI 2025 · similarity 0.88)
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