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About Slides On ARIMA Models - Robert Nau
This document provides an overview of ARIMA models for time series forecasting. It defines what ARIMA stands for (Auto-Regressive Integrated Moving Average) and explains the components - differencing (I) to make a time series stationary, autoregressive terms (AR) using lags of the differenced series, and moving average terms (MA) using lags of forecast errors. The document outlines the steps for constructing an ARIMA model, including identifying the appropriate AR and MA terms using ACF and PACF plots. It also discusses interpreting AR and MA behavior and provides examples of identifying ARIMA models for different time series patterns.
Detailed Information
| Author: | ['Kevin Bunyan'] |
|---|---|
| Publication Year: | 2016 |
| Pages: | 21 |
| Language: | English |
| Format: | |
| Price: | FREE |
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