What Are the Different Types of Quantitative Forecasting Techniques?

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  • Written By: Kaiser Castro
  • Edited By: C. Wilborn
  • Last Modified Date: 23 August 2019
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Quantitative forecasting techniques typically call for the analysis of statistics and raw data. The simple moving method, weight moving method, exponential smoothing method, and time series analysis are quantitative forecasting techniques that are usually used by economists and data analysts. These techniques are used to evaluate numerical data while considering changes in trends. Accurate forecasting is used by businesses to help make sound business decisions.

The simple moving method of forecasting is a form of quantitative research that is based on an adjustable set period. This method is used to show trends over a period of time by evaluating raw data, usually over the course of 30 days or many months. Every month, the older information is replaced with the information of the new month. For example, if data is evaluated over the course of August and September, then the numbers from August will be removed and be replaced by September's information to see if there are any trends in the data.


Similar to the simple moving method, a weight moving method dissects the information during an evaluation period but with different weights given to each month. This method of data evaluation is usually used to evaluate trends with expected monthly changes; the sales of seasonal clothing, for example, can benefit from these types of quantitative forecasting techniques. If an economist predicts that more people will be purchasing shorts during the summer months, a standard multiplier can be applied to this window of time, which will typically increase the accuracy of budget estimates during those months.

These quantitative forecasting techniques tend to focus on older data. The exponential smoothing method evaluates more recent information. This method is good for researching data that changes rapidly, such as sales figures in a temperamental market. For example, if a business analyst is trying to predict next month's sales, then exponential smoothing will call upon the data on the recent days leading to this new month to predict projected sales.

Quantitative forecasting techniques will sometimes call for analyzing time series. A time series is an observation of data at different points in time. Examples include analysis of daily stock prices, weekly sales goals, and monthly expenses. These techniques examine the underlying context of data over a large period of time. This technique usually measures historical data using line charts to forecast future events, allowing an economist to identify characteristics in data that can be used in making predictions about future outcomes.


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