Executive Opinion: Aggregating the opinions and insights of senior management and executives

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Experts are then encouraged to revise their earlier answers in light of the replies of other members of their panel.
Market Research: Gathering consumer opinions and preferences through surveys, focus groups, and interviews to gauge future demand for products or services.
Sales Force Composite: Combining the individual sales forecasts of salespeople, who have direct contact with customers and market conditions.

2. Quantitative Forecasting Methods:
These methods use mathematical and statistical nurse database techniques to analyze historical data and extrapolate future trends. They are best suited when sufficient historical data is available and when the relationship between variables is stable.


Time Series Methods:

Moving Averages: Calculating the average of a specific number of past data points to predict the next period. Simple, but can be slow to react to changes.
Exponential Smoothing: Similar to moving averages but gives more weight to recent data, making it more responsive to changes.
ARIMA (Autoregressive Integrated Moving Average): A powerful and complex statistical model that captures various time series patterns (trends, seasonality, cycles) and is widely used for short- to medium-term forecasting.
Seasonal Decomposition: Breaking down a time series into its trend, seasonal, and irregular components to better understand and forecast patterns.
Causal/Associative Methods:

Regression Analysis: Identifying a statistical relationship between the variable being forecasted (dependent variable) and one or more independent variables (e.g., predicting sales based on advertising spend or economic indicators).