Times series analysis is historical data such as trends

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Questions

According to (Jacobs and Jacobs, 2020) there are four basic types of forecasting: qualitative, time series analysis, casual relationships, and simulation.

Times series analysis is historical data such as trends, seasonal or cyclical influences that can predict future demand. If you were to be projecting a future monthly sales performance, you can gather data for the past two years and see what those numbers projected to get a better prediction.

Casual forecasting uses linear regression techniques that are related to factors in the environment. Casual forecasting uses other than past demand in making the forecast.

Simulation methods run experiments to generate data such as testing hypotheses, evaluating alternatives, and improving performance. Simulation is composed of different mathematical formulas, logical rules, and historical data (Jacobs & Jacobs).

Qualitative forecasting makes predictions on a company's finance and is crucial for developing projects like marketing campaigns. qualitative forecasting is flexible in its ability to use other sources other than numerical data.

Reference no: EM133673126

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