TOKON: TOKenization-Optimized Normalization for time series analysis with a large language model
This work addresses the problem of limited versatility of large language models in time series analysis, which is significant for researchers and practitioners working with time series data, and presents an incremental improvement.
The authors tackled the limitation of large language models in analyzing time series data and achieved a 7% to 18% improvement in root mean square error (RMSE) for multi-step forecasting. This was accomplished through the proposed Tokenization-Optimized Normalization (TOKON) technique and a novel prompt for time series forecasting.
While large language models have rapidly evolved towards general artificial intelligence, their versatility in analyzing time series data remains limited. To address this limitation, we propose a novel normalization technique that considers the inherent nature of tokenization. The proposed Tokenization-Optimized Normalization (TOKON) simplifies time series data by representing each element with a single token, effectively reducing the number of tokens by 2 to 3 times. Additionally, we introduce a novel prompt for time series forecasting, termed Time Series Forecasting with Care (TFSC), to further enhance forecasting performance. Experimental results demonstrate that TOKON improves root mean square error (RMSE) for multi-step forecasting by approximately 7% to 18%, depending on the dataset and prompting method. Furthermore, TFSC, when used in conjunction with TOKON, shows additional improvements in forecasting accuracy for certain datasets