This document provides an overview of time series analysis and ARIMA modeling in R. It includes notes on the components of time series data, including trends, seasonality, and irregular components. It also lists two video links - a lecture on time series analysis and a 10-minute tutorial on performing ARIMA modeling in R. The overall goal is to provide resources for understanding time series analysis and forecasting from zero knowledge to using ARIMA models in R.

1. Steps to ARIMA in R (work in
progress)
From ZERO background to usable forecast in R

2. This is just a set of notes

3. Notes on Time Series
• Next slide taken from
www.ams.sunysb.edu/~zhu/ams586/Time_Series_Introduction.pptx

4. Time-Series Data
• Numerical data obtained at regular time intervals
Time Series
Cyclical
Component
Irregular /Random
Component
Trend Component Seasonal
Component
Overall,
persistent, long-
term movement
Regular periodic
fluctuations,
usually within a
12-month period
Repeating swings
or movements
over more than
one year
Erratic or residual
fluctuations

5. Video Lectures and Tutorials
• Time Series Lecture by Prof Arun Kanda
• http://www.youtube.com/watch?v=zlZaOnBbpUg
• ARIMA in R (10 mins video)
• http://www.youtube.com/watch?v=zFo7QixEKvg