Forecasting Methods Help

What is Forecasting?

Quite often, past events have been used as a prediction of what will happen in the future. The weather conditions, market trends, and financial performance, among others, are some known predictions. Even most currently, sporting investments are being made based on past occurrences. Forecasting is the prediction of what is likely to happen in the future based on past and present events. Businesses find forecasting an essential tool in their daily decision making. Since companies face numerous uncertainties, the use of historical data and trends has helped them solve related problems. Forecasting is a fundamental tool for planning.

Primary and secondary sources give the data used for forecasting. Primary sources provide the first-hand information. The forecaster chooses whether to use interviews, questionnaires or focus groups to collect primary data. Secondary sources, on the other hand, use data that has already been collected and published. Secondary data is quicker to obtain the primary data. Use of secondary data makes compilation and analysis quicker. Primary sources are known to produce more reliable data, even if the collection process is slower.

Types of Forecasting Methods

To predict what is likely to happen in the future, various ways are applicable. Forecasting methods help available in mypaperhelpers.com provides an in-depth understanding of these methods and others as well.

  1. Qualitative forecasting

These are non-mathematical methods, based on personal opinions. They are characterized by immense bias since there is rarely any data involved. The expert’s knowledge, intuition, and experience form the basis for decision making. Delphi method, scenario building, statistical surveys and composite forecasts are examples of qualitative forecasting.

  1. Quantitative methods

 Unlike qualitative technique, quantitative forecasting uses mathematical processes. It is, therefore, a consistent and objective approach, with less bias. Decisions are dependent on available data and figures, which can be mathematically verified. Simple exponential smoothing, multiplicative seasonal indexes, simple and weighted moving averages are quantitative forecasting methods.

  1. Naive forecasting methods

These methods base future projections on data recorded for a past period. This forecasting method makes no adjustments for seasonal variations or cyclical trends that may change previous data. It does not concentrate on the cause-effect relationship. This method is used to create forecasts for sophisticated forecasting techniques.

  1. Causal forecasting methods

These are methods that predict a variable using underlying factors. Examples of causal forecasting methods are regression analysis and some autoregressive moving averages. These methods take assumptions that mathematical functions with current variables can be used to forecast the future value of the variable. 

  1. Time series forecasting methods

These are methods that employ historical data to estimate the future outcome. They include techniques such as exponential smoothing, moving average and trend analysis.   A time series is a group of data recorded over a specified period. Example of a time series is the monthly production of an organization over the past ten years. Long term forecasts are used to predict future outcomes. These projections aid an organization in planning. 

  1. Cross-impact matrix method

This method recognizes that the occurrence of an event can affect the likelihoods of other events. Probabilities are assigned to represent the likelihood of occurrence of the event in the presence and absence of other events. The results are then used to establish the overall relationship of the system. 

  1. Scenario method 

It is a narrative forecast that describes a potential course of events. The scenario describes the impact on the other components and the system as a whole. It is a “script” for defining the particulars of an uncertain future. Forecasters write scenarios as a long term prediction of the future. Scenarios get decision makers to think about how to handle both pessimistic and optimistic occurrences. 

  1. Decision trees

Decisions trees were in the past simple illustrations showing structural relationships. However, they have evolved over the years, even to become the basis for computer flow charts. With computer technology, decision trees can come up with complex relationships that are assigned probabilities. Decision trees make use of the concept of expected value. There is even application of Bayes Theorem, helping decision makers come up with perfect solutions.

Features of Forecasting

The following are features of a forecast:

  • Involves future events
  • Based on past and present events
  • Uses forecasting techniques

 

The Process of Forecasting

Forecasters ought to follow a given procedure to obtain accurate results. 

  1. Develop a basis for prediction

Developing a basis for prediction involves establishing why an investigation is justified. It also entails identifying the current market position of the company required.

  1. Estimate future operations of the business

This step involves estimating the industry’s future conditions. By analysing the industry, it is possible to predict the individual company’s performance.

  1. Regulate the forecast

This process involves a comparison of the company’s actual happenings with the different estimates in the past. Forecasters then identify the differences in results and reasons for deviations 

  1. Review the process

Here, the forecasters check each step of the process and make any necessary adjustments.

Detailed Forecasting Process

Importance of Forecasting

  • Pivotal role in an Organization
  • Key to Success 
  • Development of a business 
  • Effective control
  • Implementation of the project
  • Co-ordination
  • Primacy to Planning

Applications of Forecasting 

  1. Supply chain management includes the movement and storage of raw materials, work-in-process inventory, and finished goods from the point of origin to the point of consumption. 
  2. Economic forecasting is the process of making predictions about the economy 
  3. Earthquake Forecasting: Defined as the specification of the time, location, and magnitude of a future earthquake within stated limits, and particularly of the next strong earthquake to occur in a region 
  4. Land Use Forecasting undertakes to project the distribution and intensity of trip generating activities in the urban area 
  5. Player & Team Performance in Sports: PECOTA, is a sabermetric system for forecasting Major League Baseball player performance 
  6. Political Forecasting aims at predicting the outcome of elections 

There are inexhaustible applications and benefits of forecasting. The decision-making process is dependent on forecasting. People seek to be better and more accurate decision makers with emerging technologies. This has increased the search for forecasting methods help.

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