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 the ever-evolving landscape of capital infrastructure projects, government agencies find themselves performing an intricate dance. The heightened focus on the timely and budget-conforming ...
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 ...
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 ...
Bayesian approaches to the study of politics are increasingly popular. But Bayesian approaches to modeling multiple time series have not been critically evaluated. This is in spite of the potential ...
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 ...
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