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 ...
In forecasting economic time series, statistical models often need to be complemented with a process to impose various constraints in a smooth manner. Systematically imposing constraints and retaining ...
Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now In the wake of the disruptive debut of ...
Time series forecasting requires simplifying complex environments into quantifiable variables. These simplifications, while ...
We introduce the performance-based Shapley value (PBSV) to measure the contributions of individual predictors to the out-of-sample loss for time-series forecasting models. Our new metric allows a ...
What if you could predict the future—not just in abstract terms, but with actionable precision? From forecasting energy demand to anticipating retail trends, the ability to make accurate predictions ...
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