By S. K. Regonda, B. Rajagopalan, U. Lall, M. Clark, and Y.-I. Moon. Published in Nonlinear Processes in Geophysics (Part of Special Issue “Nonlinear deterministic dynamics in hydrologic systems: ...

Developed a suite of Python notebooks and hands-on exercises that guide the full modeling lifecycle—from exploratory visualization and simple linear fits to advanced seasonal decomposition and mode ...

The causality analysis of multivariate time series and formation of complex networks relies on the estimation of the direct cause-effect from one observed variable to another accounting for the ...

@article{bloemheuvel2022graph, title={Graph neural networks for multivariate time series regression with application to seismic data}, author={Bloemheuvel, Stefan and van den Hoogen, Jurgen and ...

Time series uses methods such as smoothing, decomposition, autocorrelation, and ARIMA models, while regression uses methods such as linear, logistic, polynomial, and multivariate models.

Neural Network Time Series Regression The data comes from a benchmark dataset that you can find in many places on the Internet by searching for "airline passengers time series regression." The raw ...

Regonda, S., B. Rajagopalan, U. Lall, M. Clark and Y. Moon, Local polynomial mehtod for ensemble forecast of time series, Nonlinear Processes in Geophysics, Special issue on "Nonlinear Deterministic ...

The causality analysis of multivariate time series and formation of complex networks relies on the estimation of the direct cause-effect from one observed variable to another accounting for the ...