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 ...
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 ...
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 ...
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 ...
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 ...
Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. In this episode, Thomas Betts chats with ...
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 ...
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Why do European and US weather models differ? Will AI help forecasting? Our meteorologists weigh in
WRAL meteorologists Elizabeth Gardner and Grant Skinner break down the ingredients of predictive weather models, explaining ...
The open-source AI models are designed to enhance the speed and accuracy of forecasting.
Researchers in China conceived a new PV forecasting approach that integrates causal convolution, recurrent structures, attention mechanisms, and the Kolmogorov–Arnold Network (KAN). Experimental ...
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