Quantitative forecasting techniques analyze raw data and statistics to evaluate numerical data and trends. Simple move, weight move, exponential smoothing, and time series analysis are commonly used. The Simple Rolling Forecast Method evaluates trends over an adjustable set period, while the weight movement method dissects data over one testing period with different weights given each month. Exponential smoothing evaluates more recent information, while time series analysis measures historical data to forecast future events.
Quantitative forecasting techniques generally require the analysis of raw data and statistics. Simple move method, weight move method, exponential smoothing method, and time series analysis are quantitative forecasting techniques generally used by economists and data analysts. These techniques are used to evaluate numerical data while considering changes in trends. Businesses use accurate forecasting to help make sound business decisions.
The Simple Rolling Forecast Method 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, typically over the course of 30 days or many months. Each month, the old information is replaced with the new month’s information. For example, if the data is evaluated over the course of August and September, the August numbers will be removed and replaced with the September information to see if there are any trends in the data.
Similar to the simple movement method, a weight movement method dissects the data over one testing period but with different weights given each month. This data evaluation method is generally used to evaluate trends with expected monthly changes; Seasonal clothing sales, for example, can benefit from these types of quantitative forecasting techniques. If an economist predicts that more people will buy shorts during the summer months, a standard multiplier can be applied to this time window, which will generally 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 investigating rapidly changing data, such as sales figures in a temperamental market. For example, if a business analyst is trying to predict sales for the next month, then the exponential fit will use data from the last few days leading up to this new month to predict projected sales.
Quantitative forecasting techniques sometimes require 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 targets, and monthly expenses. These techniques examine the underlying context of the data over a long period of time. This technique typically measures historical data using line charts to forecast future events, allowing an economist to identify features in the data that can be used to make predictions about future outcomes.
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