Modern strategies for time series regression

10/29/2020
by   Stephanie Clark, et al.
University of Technology Sydney
47

This paper discusses several modern approaches to regression analysis involving time series data where some of the predictor variables are also indexed by time. We discuss classical statistical approaches as well as methods that have been proposed recently in the machine learning literature. The approaches are compared and contrasted, and it will be seen that there are advantages and disadvantages to most currently available approaches. There is ample room for methodological developments in this area. The work is motivated by an application involving the prediction of water levels as a function of rainfall and other climate variables in an aquifer in eastern Australia.

READ FULL TEXT

page 1

page 2

page 3

page 4

05/22/2023

Conditional normalization in time series analysis

Time series often reflect variation associated with other related variab...
09/28/2022

Experimental study of time series forecasting methods for groundwater level prediction

Groundwater level prediction is an applied time series forecasting task ...
08/18/2023

Time Series Predictions in Unmonitored Sites: A Survey of Machine Learning Techniques in Water Resources

Prediction of dynamic environmental variables in unmonitored sites remai...
09/26/2022

Modeling Polyp Activity of Paragorgia arborea Using Supervised Learning

While the distribution patterns of cold-water corals, such as Paragorgia...

Please sign up or login with your details

Forgot password? Click here to reset