Exploring Data Pattterns and Choosing a Forecasting Technique

Exploring Data Patterns and Choosing a

Forecasting Technique

Introduction

One of the most time-consuming and difficult parts of forecasting is the collection of valid and reliable data. Data processing personnel are fond of using the expression “garbage in Garbage out” (GIGO). This expression also applies to forecasting. A forecast can be no more accurate than the data on which it is based. The most sophisticated model will fail if it is applied to unreliable data.

The advent of computers has helped generate an incredible accumulation of data on all most all subjects. The difficult task facing most forecasters is how to find relevant data that will help solve their specific decision-making problems.

Criteria to determine the data use fullness and types of data

Four criteria can be applied to the determination of whether data will be useful:

1. Data should be reliable and accurate. Proper care must taken that data are collected from q reliable source with proper attention given to accuracy.

2. Data should be relevant. The data must be representative of the circumstances for which they are used.

3. Data should be consistent. When definitions change concerning how data are collected, adjustments need to be made to retain consistency in historical patterns. This can be a problem, for example, when government agencies change the mix or “ market basket” used in determining cost-of-living index. Thirty years ago personal computers were not part of the mix of products being purchased by consumers: now they are.

4. Date should be timely. Data collected, summarized, and published on a timely basis will be of great value to the forecaster. There can be too little data (not enough history on which to base future outcomes) or too much data ( data from irrelevant historical periods far in the past).

Types of data

Generally, two types of data are of interest to the forecaster. The first are the data collected at a single point in time, be it an hour, a day, a week, a month, or a quarter. The second are observations of data made over time. When all observations are from the same time period, we call them cross-sectional data. The objective is to examine such data and then to extrapolate or extend the revealed relationships to a larger population. Drawing a random sample of personnel files to study the circumstances of the employees of a company is one example. Gathering data on the age and current maintenance cost of nine vehicles as a sample of Transit Authority’s buses is another. A scatter diagram helps one to visualize the relationship and may suggest age might be used to help in forecasting the annual maintenance budget.