Deep TPC: Temporal-Prior Conditioning for Time Series Forecasting
arXiv:2602.16188v1 Announce Type: new Abstract: LLM-for-time series (TS) methods typically treat time shallowly, injecting positional or prompt-based cues once at the input of a largely frozen decoder, which limits temporal reasoning as this information degrades through the layers. We introduce Temporal-Prior Conditioning (TPC), which elevates time to a first-class modality that conditions the model at multiple depths. TPC attaches a small set of learnable time series tokens to the patch stream; at selected layers these tokens cross-attend to temporal embeddings derived from compact, human-readable temporal descriptors encoded by the same frozen LLM, then feed temporal context back via self-attention. This disentangles time series signal and temporal information while maintaining a low parameter budget. We show that by training only the cross-attention modules and explicitly disentangling time series signal and temporal information, TPC consistently outperforms both full fine-tuning a
arXiv:2602.16188v1 Announce Type: new Abstract: LLM-for-time series (TS) methods typically treat time shallowly, injecting positional or prompt-based cues once at the input of a largely frozen decoder, which limits temporal reasoning as this information degrades through the layers. We introduce Temporal-Prior Conditioning (TPC), which elevates time to a first-class modality that conditions the model at multiple depths. TPC attaches a small set of learnable time series tokens to the patch stream; at selected layers these tokens cross-attend to temporal embeddings derived from compact, human-readable temporal descriptors encoded by the same frozen LLM, then feed temporal context back via self-attention. This disentangles time series signal and temporal information while maintaining a low parameter budget. We show that by training only the cross-attention modules and explicitly disentangling time series signal and temporal information, TPC consistently outperforms both full fine-tuning and shallow conditioning strategies, achieving state-of-the-art performance in long-term forecasting across diverse datasets. Code available at: https://github.com/fil-mp/Deep_tpc
Executive Summary
This article presents Temporal-Prior Conditioning (TPC), a novel approach to time series forecasting that elevates time to a first-class modality, conditioning the model at multiple depths. TPC attaches learnable time series tokens to the patch stream, which cross-attend to temporal embeddings derived from compact temporal descriptors. This disentangles time series signal and temporal information while maintaining a low parameter budget. The authors demonstrate that TPC outperforms full fine-tuning and shallow conditioning strategies, achieving state-of-the-art performance in long-term forecasting across diverse datasets. The approach is particularly relevant to applications where temporal context is critical, such as finance and healthcare. The code is available on GitHub, facilitating replication and further research. The results have significant implications for the field of time series forecasting, highlighting the importance of temporal reasoning and the potential benefits of disentangling time series signal and temporal information.
Key Points
- ▸ Temporal-Prior Conditioning (TPC) is a novel approach to time series forecasting that elevates time to a first-class modality.
- ▸ TPC conditions the model at multiple depths, using learnable time series tokens and temporal embeddings.
- ▸ The approach disentangles time series signal and temporal information while maintaining a low parameter budget.
- ▸ TPC outperforms full fine-tuning and shallow conditioning strategies in long-term forecasting across diverse datasets.
Merits
Improved Temporal Reasoning
TPC's ability to condition the model at multiple depths and disentangle time series signal and temporal information enables improved temporal reasoning, leading to better forecasting performance.
Low Parameter Budget
TPC maintains a low parameter budget, making it a more efficient and scalable approach to time series forecasting compared to full fine-tuning and shallow conditioning strategies.
Demerits
Complexity
TPC's architecture is more complex compared to traditional time series forecasting methods, which may require additional computational resources and expertise to implement.
Limited Generalizability
The performance of TPC may be limited to specific datasets and applications, requiring further research to establish its generalizability across different domains.
Expert Commentary
The article presents a novel and innovative approach to time series forecasting, addressing the limitations of traditional methods in handling temporal context. The results demonstrate the effectiveness of TPC in achieving state-of-the-art performance in long-term forecasting across diverse datasets. However, the complexity of the approach and limited generalizability of the results require further research to establish its practical applicability and scalability. The use of a frozen LLM to encode compact temporal descriptors is an interesting application of NLP techniques in time series forecasting and warrants further investigation.
Recommendations
- ✓ Future research should focus on establishing the generalizability of TPC across different domains and datasets.
- ✓ The approach should be compared to other state-of-the-art methods in time series forecasting to further establish its effectiveness and limitations.
