Nature

A novel LLM time series forecasting method based on integer-decimal decomposition

In the era of big data, systems and entities continuously evolve, generating vast amounts of time series data across various fields. Analyzing these data is crucial for extracting valuable information ...
Nature

Improving time series forecasting in frequency domain using a multi resolution dual branch mixer with noise insensitive ArcTanLoss

Enhancing the accuracy of long-term time series forecasting is a crucial task across various fields. Recently, many studies have employed the Discrete Fourier Transform (DFT) to convert time series ...
Investopedia

Understanding Time Series: Analyzing Data Trends Over Time

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...
Forbes

IBM Improves Generative AI Forecasting Using Time, Not Just Attention

According to IBM, attention is not all you need when forecasting certain outcomes with generative AI. You also need time. Earlier this year, IBM made its open-source TinyTimeMixer (TTM) model ...