Qualitative vs. Quantitative
Methods
Qualitative forecasting techniques are subjective, based on the opinion and
judgement of consumers, experts; appropriate when past data is not available.
It is usually applied to intermediate-long range decisions.
Example of qualitative forecasting methods:
1. Informed opinion and judgment
2. Delphi method
3. Market research
4. Historical life-cycle Analogy.
Quantitative forecasting models are used to estimate future demands as a function of past data; appropriate when past data is available. It is usually applied to short-intermediate range decisions. Example of Quantitative forecasting methods:
1. Last period demand
2. Arithmetic Average
3. Simple Moving Average (N-Period)
4. Weighted Moving Average (N-period)
5. Simple Exponential Smoothing
6. Multiplicative Seasonal Indexes
Naïve Approach
Naïve forecasts are the most cost-effective and efficient objective forecasting model, and provide a benchmark against which more sophisticated models can be compared. For stable time series data, this approach says that the forecast for any period equals the previous period's actual value.
Time series methods
Time series methods use historical data as the basis of estimating future outcomes.
1. Moving average
2. Weighted moving average
3. Exponential smoothing
4. Autoregressive moving average
5. Autoregressive integrated moving average
6. Extrapolation
7. Linear prediction
8. Trend estimation
9. Growth curve
Casual / econometric forecasting methods
Some forecasting methods use the assumption that it is possible to identify the underlying factors that might influence the variable that is being forecast. For example, including information about weather conditions might improve the ability of a model to predict umbrella sales. This is a model of seasonality which shows a regular pattern of up and down fluctuations. In addition to weather, seasonality can also be due to holidays and customs such as predicting that sales in college football apparel will be higher during football season as opposed to the off season. Casual forecasting methods are also subject to the discretion of the forecaster. There are several informal methods which do not have strict algorithms, but rather modest and unstructured guidance. One can forecast based on, for example, linear relationships. If one variable is linearly related to the other for a long enough period of time, it may be beneficial to predict such a relationship in the future. This is quite different from the aforementioned model of seasonality whose graph would more closely resemble a sine or cosine wave. The most important factor when performing this operation is using concrete and substantiated data. Forecasting off of another forecast produces inconclusive and possibly erroneous results. Such methods include:
1. Regression analysis includes a large group of methods that can be used to predict future values of a variable using information about other variables. These methods include both parametric (linear or non-linear) and non-parametric techniques.
2. Autoregressive moving average with exogenous inputs
Judgmental methods
Judgmental forecasting methods incorporate intuitive judgements, opinions and subjective probability estimates.
1. Composite forecasts
1. surveys
2. Delphi method
3. Scenario building
4. Technology forecasting
5. Forecast by analogy
6. Artificial intelligence methods
7. Artificial neural networks
8. Group method of data handling
9. Support vector machines
Other methods
1. Simulation
2. Prediction market
3. Probabilistic forecasting and ensemble forecasting
4. Reference class forecasting