Forecasting techniques have been used for the longest period of time, in fact, as long as humanity has existed. The mystery of the unknown is unsettling and we all in some way want to be able to predict the future. From tarot cards and palm reading, to weather forecasting and predicting economic trends, forecasting methods are as varied as the forecasting ideas behind them.
In the business world, forecasting is a statistical method that employs a time series analysis to mitigate risk and uncertainty. Although it does involve some level of risk and uncertainty, making accurate forecasts in business is vital.
Businesses rely on business analytics for growth because analytics provide accurate forecasting for decision making, which impacts productivity and revenues. Human resource management professionals also use forecasting techniques to predict employee job satisfaction, tenure, or turnover.
In this article, we will explore forecasting techniques, how to learn forecasting, how to conduct accurate forecasting using reliable forecasting methods, and provide you with some forecasting ideas for your next project.
Best Forecasting Technique Examples
According to Statista, 65 percent of businesses worldwide see big data analytics as critical or very important in forecasting for their organization. Forecasting techniques use data from a particular time period to predict future outcomes. Companies use data analytics to make sales forecasts and predict consumer demand whenever they develop new products.
There are two main types of models of forecasting, namely qualitative and quantitative. When no previous data is available, qualitative forecasting is used. When launching a new product or looking for a new business location, for example. When historical data is available, quantitative forecasting is used. This method relies on mathematical statistics.
In business, there are many dependent and independent variables that determine growth. The forecasting model chosen is determined by factors such as the availability of historical data, the accuracy required for the forecast, the context of the forecast, and the forecasting time required to complete the exercise. Below we shall explore some examples of forecasting techniques.
Simple Linear Regression
Regression analysis uses a statistical method to sort out the relationship between the many factors affecting business growth and the direct impact they may have on business. Simple linear regression takes into consideration one dependent variable against one independent variable. For example, comparing the effects of running an ad campaign for a specific product or service on revenue.
Multiple Linear Regression
This is a statistical method similar to the simple regression analysis method mentioned above. The difference here is that one dependent variable considers additional independent variables to predict outcomes.
For example, determining whether the revenue generated by running an ad campaign was worth the money spent on promotion, advertising, and the time spent on running the ad campaign.
Straight Line Forecasting
This technique is perhaps the simplest of methods to use as it is based on averages. It uses an assumed time series analysis to predict future outcomes. In sales, for example, a growth target is set using data averages and percentages from past trends and figures. These figures are then used to predict future revenue assuming that sales remain constant.
This technique is a time series model that uses a moving average to make predictions. It is a form of short-term forecasting, as it assists with predictions ranging from three to five months. When straight line forecasting does not produce accurate and reliable data, this forecasting procedure is used.
The use of underlying average data is used to eliminate data in the extremes, resulting in a stable model upon which to make predictions. Exponential smoothing can sometimes be used with this model to prioritize more recent data sets.
Market research is used to predict the future demand of goods or services. It is employed when historical data is unavailable. This method uses fresh data generated through data collection techniques, such as surveys, to establish a baseline against which predictions and decisions can be made. Many companies use market research to predict future sales of new products.
When historical data is unavailable, data regarding future trends can be gathered from experts in a field rather than relying on market research. The Delphi technique generates data for forecasting by relying on expert consensus rather than personal judgment and opinion. This method is particularly useful when the forecast requires current or ‘insider’ information, and can be a form of causal forecasting.
This technique is a time series method used in macroeconomic forecasting. It uses present or current data to project outcomes without considering any causal variables that may have influenced the baseline data. This method may incur forecast errors and require an error analysis margin in order to justify the conclusions.
The drift technique is similar to the naive technique because it does not adjust for causal variables. Instead, this method observes the rise and fall of predictions based on a fixed average demand or change observed from historical data.
This technique is used in financial time series forecasting with prediction intervals. This could be an hourly prediction or a monthly prediction. This technique is used in stock markets or similar financial markets.
How to Build a Simple Linear Regression Model
- Determine Your Variables. To build a simple linear regression model, you will need two correlated variables. This means that the independent variable must be related to the dependent variable. It may not necessarily be the cause of the dependent variable.
- Create a Simple Linear Equation. This is a statistical technique that will result in the generation of a graph. As a result, you’ll need a simple linear equation that represents your variables and can be solved with ease and acceptable accuracy. For example, if your variables are comparing height against weight, and you use ‘‘x’’ to represent height and ‘‘y’’ to represent weight, your equation might read, y=10x+2.
- Solve the Equation Using Data Points. You can apply analytical techniques here by creating a range of data. You could use heights ranging between four and a half feet to seven feet. Then, create prediction intervals of an inch or half an inch in order to generate sufficient data points.
- Draw a Line on a Graph. Using the results generated from solving the equation, draw a line on a simple graph. This will help you visualize the data you are forecasting. Additionally, this will provide the accurate forecasting model you will need in the next step.
- Forecast. Using the data points represented on the graph, you can forecast weights of people whose height may exceed seven feet by extending the line beyond the seven foot data point.
Forecasting Ideas: Top 5 Tips to Master Forecasting
Although forecasting involves risk, it does not mean that it is impossible to learn. In business, predictions tend to employ statistical methods, as these tend to be more reliable. You can use tools such as a flow chart or graph to illustrate your predictions. Additionally, using the absolute percentage error ratio, you will be able to determine the accuracy of your forecasts.
Considering that technology today relies heavily on forecasting methods to create, inform, and improve on artificial intelligence, you can learn artificial intelligence and put your forecasting skills to use. For example, power plants rely on short-term load forecasting techniques in their energy management systems. Let us take a look at some tips and guidelines you can use to master forecasting techniques.
Determine Why You Are Forecasting
This is perhaps the most central factor that will influence your forecast technique. Single methods may not be sufficient as there are many factors that will influence your success. Before selecting a model and deciding on a technique, consider the human resource requirements, the factors that led to the forecasting exercise, and previous or historical data.
Determine What You Are Forecasting
The forecasts you create should be solution-oriented regardless of whether the forecast is for business or not. The objective function of econometric variables should be relevant in generating desired outcomes. For business, this may mean reflected revenues and growth, whereas for a group of social media influencers, this may be a membership function of increased followers.
Determine Whom You Are Forecasting For
You need to understand the group of people you are building forecasts for. If you are dealing with business executives in charge of decision making, the model you use will be different than when informing a group of social workers. To account for unexpected variables in the data, your forecasts may also require an error analysis, such as the absolute percentage error ratio.
Determine Where You Are Forecasting
You must be aware of the location where your forecast is expected to have an impact. In sales, your predictions for a physical clothing outlet may differ from your predictions for an online store. The variables in each of these instances will produce different results.
Knowing where your audience or prospective customer base is located will determine profitability. Forecasting errors potentially lead to losses on revenue.
Determine How You Are Forecasting
You will need to know what forecast models have been used in the past to make a predictive analysis. Without this fundamental analysis, you will end up with fuzzy input. It is necessary to consider which forecast models will produce the best results. Forecasting does not always apply single methods. It sometimes relies on a hybrid model to be effective.
Are Forecasting Techniques Worth Learning?
Yes, forecasting techniques are worth learning. Forecasting techniques provide information about future trends and economic flow so companies can make more informed decisions. An effective forecasting procedure and statistical technique will enable businesses to increase revenues and grow.
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Christopher Sims, a member of the National Bureau of Economic Research (NBER) and professor at Princeton University won a Nobel prize for his extensive research in macroeconomic forecasting.
Christopher Sims played a key role in exploring how changes in inflation, investment, gross domestic product, and unemployment interact with unexpected events resulting in short-term economic consequences and influencing long-term government economic policy.
Further, the 11th European Central Bank Conference on Forecasting Techniques explored how to obtain meaningful forecasts after floods and pandemics in light of the COVID-19 pandemic. These two examples demonstrate the key role that forecasting plays not just in small businesses, but for entire economies.
Forecasting Techniques FAQ
The most accurate forecasting model is ideally a statistical technique because it gives the most acceptable accuracy on data. You can learn predictive analytical techniques for forecasting the future for a career in finance or risk management.
Yes, companies use forecasting applications that rely on big data to make decisions. This has decreased the amount of time it takes to complete a forecast cycle, where software can now produce forecasts on an hourly prediction basis. Forecasting applications have also reduced the frequency of forecast errors.
The best methods in the forecasting field are based on the purpose the forecasting is serving. Single methods of forecasting are no longer used as they do not account for forecast errors. You can use qualitative, quantitative, or hybrid methods. A time series model additionally generates a comparative analysis from which to build an accurate forecasting model.
To make predictions using an artificial neural network, machine learning employs software-based forecasting applications. The software combines hybrid prediction methods to create a generalized neural network with artificial learning ability. This deep learning capability allows the machine to predict outcomes without being explicitly programmed.
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