“In the case of quantitative methods of forecasting, each technique makes explicit assumptions about the underlying pattern”

Indeed, in quantitative methods of forecasting, each technique is built upon explicit assumptions about the underlying patterns within the data.

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These assumptions guide the selection and application of specific forecasting methods. Here are some common quantitative forecasting methods and their associated assumptions:

1. Time Series Analysis:

• Assumption: Assumes that the historical pattern observed in a time series will continue into the future.
• Example: ARIMA models assume that a time series can be decomposed into trend, seasonality, and random components.

1. Exponential Smoothing Models:

• Assumption: Assumes that recent observations are more indicative of future trends than older ones.
• Example: Simple Exponential Smoothing assigns exponentially decreasing weights to past observations.

1. Linear Regression:

• Assumption: Assumes a linear relationship between the independent variable(s) and the dependent variable.
• Example: The assumption that changes in the predictor variable(s) are associated with a proportional change in the response variable.

1. Causal Models:

• Assumption: Assumes that the variable being forecasted is influenced by one or more other variables in a causal manner.
• Example: Multiple Regression assumes a causal relationship between the dependent variable and multiple independent variables.

1. Seasonal Decomposition of Time Series (STL):

• Assumption: Assumes that a time series can be decomposed into seasonal, trend, and residual components.
• Example: Decomposing a monthly sales time series into its seasonal sales patterns and overall trend.

1. Box-Jenkins (ARIMA):

• Assumption: Assumes that a time series can be modeled as a combination of autoregressive (AR), moving average (MA), and differencing components.
• Example: Using differencing to stabilize the mean of a time series before applying autoregressive and moving average components.

1. Neural Networks:

• Assumption: Assumes that complex relationships within the data can be captured by training a neural network model.
• Example: Using a neural network to learn intricate patterns and non-linear relationships in historical data.

Understanding these assumptions is essential for selecting the most appropriate forecasting method based on the characteristics of the data and the underlying patterns. Failing to consider these assumptions can lead to inaccurate predictions and unreliable forecasts.