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 ...
Nature

A novel extreme adaptive GRU for multivariate time series forecasting

Multivariate time series forecasting is a critical problem in many real-world scenarios. Recent advances in deep learning have significantly enhanced the ability to tackle such problems. However, a ...
InfoWorld

5 ways companies can use time-series forecasting

Recent advances in AI, such as foundation models, make it possible for smaller companies to build custom models to make predictions, reduce uncertainty, and gain business advantage. Time series ...
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 ...
InfoWorld

Time series forecasting with XGBoost and InfluxDB

XGBoost is a popular open source machine learning library that can be used to solve all kinds of prediction problems. Here’s how to use XGBoost with InfluxDB. XGBoost is an open source machine ...
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 ...