Visual Studio Magazine

Neural Network Time Series Regression Using Python

The goal of a time series regression problem is best explained by a concrete example. Suppose you own an airline company and you want to predict the number of passengers you'll have next month based ...
InfoWorld

An introduction to time series forecasting

Time series forecasts are used to predict a future value or a classification at a particular point in time. Here’s a brief overview of their common uses and how they are developed. Industries from ...
Investopedia

Master ARIMA: Your Guide to Time Series Forecasting

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 ...
JSTOR Daily

A Robust Regression Model for a First-Order Autoregressive Time Series with Unequal Spacing: Application to Water Monitoring

This is a preview. Log in through your library . Abstract Time series arise often in environmental monitoring settings, which typically involve measuring processes repeatedly over time. In many such ...
JSTOR Daily

A General Approach to the Limiting Distribution for Estimators in Time Series Regression with Nonstable Autoregressive Errors

We consider the time series regression model where the error term follows a nonstable autoregressive process, and present a general approach for deriving the limiting ...
Investopedia

Stepwise Regression Explained: Uses, Benefits, and Drawbacks

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 ...