The algorithm also estimates the volatility of a multivariate time series to provide the user with a confidence level for its predictions. “Even as the time-series data becomes more and more complex, ...

Whether someone is trying to predict tomorrow’s weather or estimate a patient’s risk of developing a disease, they will likely need to interpret time-series data. To make these powerful tools more ...

By adapting a powerful algorithm, MIT researchers created a user-friendly tool that enables a nonexpert to make predictions with high accuracy using time-series data with just a few keystrokes and ...

Recently, Central Control Technology released a new upgraded version of its time-series large model TPT2 (Time-series Pre-trained Transformer), marking the official entry of industrial AI into a "time ...

On August 28, Central Control Technology officially released the Time-series Pre-trained Transformer (TPT2) model globally. This model is built on industrial time-series data to create AI algorithms ...

Our results show that bagging can achieve large reductions in prediction mean-squared errors even in such challenging applications as inflation forecasting; however, bagging is not the only method ...