Beginners guide to ARIMA: Problem Identification & Data Gathering – Part 2

In continuation to my earlier post, I’m trying to explore ARIMA using an Example. In this post we will go into each step in detail how we can accomplish ARIMA based forecasting for a problem.

Step 1: Problem Identification or Our Scenario

We are going to consider the past history of time series data on the Household Power consumption and use that data to forecast using ARIMA. There is also a research paper published in Proceedings of the International MultiConference of Engineers and Computer Scientists 2013 Vol I, on the same dataset analysing the performance between ARMA and ARIMA. Our post will focus on step by step accomplishing forecast using R on the same dataset for ease of use for Beginners. Sooner or later we will evaluate tools such as AutoBox, R which can be used for solving this problems.

Step 2: Data Gathering or Identification of dataset

The dataset we are going to use would be a dataset on Individual household electric power consumption available in UCI Repository under the URL: https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption. This dataset is a multivariate dataset. Please check this link to understand the difference between univariate, bivariate and multivariate.

Quick Summary of the Dataset:

  • Dataset contains data between December 2006 and November 2010
  • It has around 19.7 MB of Data
  • File is in .TXT Format
  • Columns in the Dataset are:
    • Date (DD/MM/YYYY)
    • Time (HH:MM:SS)
    • Global active power
    • Global Reactive power
    • Voltage
    • Global Intensity
    • Sub Metering 1
    • Sub Metering 2
    • Sub Metering 3

You can open this semicolon delimited text file in Excel and make the necessary steps on the wizard you will be having an excel sheet with the data as given below. I was able to load rows only up to 1048576. The actual total number of rows in the text file is 2075260. Whoa..

 

Next to do the Step 3: preliminary analysis we can use R as a tool. For using R as a tool we need to load this data into R and for analysing it. For this I can save this excel sheet in CSV format or in XLS format and the import into R as outlined in my other post or using this link. I’m using RStudio for the purpose and demonstrating the data loading process in the screenshots in the subsequent sections.

First the installation had shown some error, after that in the subsequent attempt the installation of gdata was successful. Now we can load the library using the command library(gdata). After we which we have loaded powerData variable with the data available in the CSV file for further analysis and we can view the data using View. Please check the console window for the code.

In the next post we will do some preliminary analysis on this data which we have loaded.

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Time Series Analysis with R: Part I

Time Series is a collection of well defined data observations over a period of time.

Examples:

  • Measuring retails sales every month for a year
  • Number of births per month in a City for a specific period
  • Age of death of successive kings of England
  • Production of gold over the years based on US Geological Survey (in Metric Tons)

Analysis of Production of Gold with R:

Please refer to the DataMarket.com for getting the gold related data. It’s free. You can refer to my previous blog to import the data from Excel. In this example I have imported the data using read.csv method using the gdata Library.


 

The above run indicates a start = 1 and End=111 with a frequency of 1.

 

Plotting of Time series data:

>plot.ts(GoldDataTS)