Time series forecasting requires simplifying complex environments into quantifiable variables. These simplifications, while ...

Time series and spatial modelling encompass a wide array of statistical and computational techniques aimed at deciphering complex temporal trends and spatial dependencies within diverse natural and ...

Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting?

SAE Transactions, Vol. 111, Section 6: JOURNAL OF PASSENGER CAR: MECHANICAL SYSTEMS JOURNAL (2002), pp. 1896-1900 (5 pages) For many years, time series analysis methods have been applied to develop ...

High-dimensional regression problems, which reveal dynamic behavior, are typically analyzed by time propagation of a few number of factors. The inference on the whole system is then based on the ...

In the 21st century, as global trade expands and cargo volumes surge, ports face mounting pressure to operate efficiently. A ...