Forecasting Techniques in Business

Qualitative techniques include soft information like human factors and personal opinions, while quantitative techniques rely on historical data or causal variables.

Trend is a long-term upward or downward movement in data, often influenced by population shifts or changing income.

The assumption is that future values of a time-series can be estimated from past values of the time-series.

Judgmental forecasts use subjective inputs like consumer opinions or expert insights to make predictions.

A naïve forecast uses a single previous value of a time series as the basis for a forecast, making the forecast equal to the previous time period's value.

Moving Average Technique averages a number of the most recent actual values equally, while Weighted Moving Average gives more weight to the most recent values in the time series.

Exponential Smoothing is a weighted averaging method that includes the previous forecast plus a percentage of the forecast error, while Moving Average Technique averages a number of the most recent values equally.

Some companies use Focus Forecasting to apply several forecasting methods to historical data and select the method with the highest accuracy for making future forecasts.

Diffusion Models are used when historical data for new products are not available, relying on rates of product adoption, market potential, mass media attention, and word-of-mouth to make predictions.

Additive Seasonality involves adding or subtracting a quantity from the time-series average, while Multiplicative Seasonality involves expressing seasonality as a percentage of the average or trend amount to multiply the series value.