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 ...
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 ...
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 ...
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 ...
JSTOR Daily

Bayesian Analysis of an Unobserved-Component Time Series Model of GDP with Markov-Switching and Time-Varying Growths

We propose an unobserved-component time series model of gross domestic product that includes Markov switching as an unobserved component. In addition to a trend component, the model has two ...
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 ...