Pre-trained foundation models are making time-series forecasting more accessible and available, unlocking its benefits for smaller organizations with limited resources. Over the last year, we’ve seen ...

Fuzzy time series forecasting models represent a versatile and robust class of predictive techniques that address uncertainty and non-linearity in data. By utilising fuzzy set theory, these models ...

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 ...

In the wake of the disruptive debut of DeepSeek-R1, reasoning models have been all the rage so far in 2025. IBM is now joining the party, with the debut today of its Granite 3.2 large language model ...

Ant International currently deploys the Falcon TST AI Model to forecast cashflow and FX exposure with more than 90% accuracy Ant International, a leading global digital payment, digitisation, and ...

Sweden’s central bank, the Riksbank, is embracing a transformative shift in economic forecasting, as recent findings from its Monetary Policy Department reveal that artificial intelligence (AI)-based ...

Meteorologists and other environmental scientists rely on numerical forecast models to aid in developing a weather outlook. These models, such as the American GFS model and European ECMWF model, use ...

1. Difference Equations -- 2. Lag Operators -- 3. Stationary ARMA Processes -- 4. Forecasting -- 5. Maximum Likelihood Estimation -- 6. Spectral Analysis -- 7 ...