Time-TK: A Multi-Offset Temporal Interaction Framework Combining Transformer and Kolmogorov-Arnold Networks for Time Series Forecasting

2026-02-13 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

Time-TK is a novel time series forecasting framework that integrates Transformer and Kolmogorov-Arnold Networks (KAN) to address limitations in existing methods, particularly their inability to capture multi-offset temporal correlations in long sequences. The framework introduces a Multi-Offset Time Embedding (MOTE) strategy, which divides input time series into multiple sub-sequences with varying time spans and performs independent embedding operations. This MOTE is then processed by a Multi-Offset Interactive KAN (MI-KAN) module, which uses Radial Basis Functions (RBFs) to model temporal patterns within each sub-sequence. A Multi-Offset Temporal Interaction (MOTI) mechanism subsequently captures cross-step dependencies and integrates global information. Evaluated on 14 real-world datasets, including traffic flow and BTC/USDT throughput, Time-TK consistently achieved state-of-the-art forecasting accuracy, outperforming 10 baseline models, including other KAN-based architectures, with statistically significant improvements in MSE and MAE.

Key takeaway

Research Scientists developing long-term time series forecasting models should investigate Time-TK's Multi-Offset Token Embedding (MOTE) and Multi-Offset Interactive KAN (MI-KAN) architecture. This approach demonstrably improves accuracy and memory efficiency over traditional Transformer and KAN-only models, especially when dealing with complex, multi-scale temporal data. You should consider integrating MOTE into your existing models to enhance their ability to leverage longer historical data without incurring substantial computational overhead.

Key insights

Multi-offset temporal embedding combined with KANs and Transformers significantly improves time series forecasting accuracy.

Principles

• Independent token embedding creates an information bottleneck for long sequences.
• Multi-offset temporal correlation is crucial for web data forecasting.
• Longer historical sequences can improve predictive performance if effectively utilized.

Method

Time-TK employs Multi-Offset Token Embedding (MOTE) to segment time series, processes these sub-sequences with a Multi-Offset Interactive KAN (MI-KAN) using RBFs, and then uses a Multi-Offset Temporal Interaction (MOTI) mechanism for global information integration.

In practice

• Use MOTE to enhance existing time series models like iTransformer or PatchTST.
• Consider KANs with RBFs for modeling nonlinear temporal patterns.
• Design embedding strategies to capture multi-scale temporal dynamics.

Topics

• Time Series Forecasting
• Kolmogorov-Arnold Networks
• Transformers
• Multi-Offset Embedding
• Web Time Series

Code references

• fsmss/Time-TK
• zhouhaoyi/ETDataset
• laiguokun/multivariate-time-series-data

Best for: Research Scientist, AI Researcher, AI Scientist, Machine Learning Engineer

Related on AIssential

• Beyond Tokenization: Direct Timestep Embedding and Contrastive Alignment for Time-Series Question Answering — Direct timestep embedding and contrastive alignment overcome LLM tokenization limits for time-series data.
• MVG-KAN: Multi-View Geo-Wind Guided KAN for PM$_{2.5}$ Forecasting — MVG-KAN integrates multi-view factors, including geo-wind, with Kolmogorov-Arnold Networks for enhanced PM$_{2.5}$ forecasting.
• Phys-JEPA: Physics-Informed Latent World Models for Multivariate Time-Series Forecasting — Phys-JEPA improves multivariate time-series forecasting by imposing physical consistency directly on latent states and transitions.
• MP3: Multi-Period Pattern Pre-training forSpatio-Temporal Forecasting — MP3 pre-trains multi-period patterns from long time series to resolve "temporal mirage" in spatio-temporal forecasting.
• Data Machina #261 — Generative AI and LLMs are being adapted to achieve state-of-the-art performance in diverse time-series tasks.
• Causal Semantic Alignment for LLM-based Time Series Forecasting — CVAformer disentangles time series variables into invariant and dynamic components to improve LLM-based forecasting accuracy by mitigating confounding effects.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.