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.
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.
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.3. 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.4. 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.5. 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 organisation over the past ten years. Long term forecasts are used to predict future outcomes. These projections aid an organisation in planning.6. Cross-impact Matrix Method
This method recognises 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.
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.8. 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.
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