Forecasting Methods & Statistics
We believe the success of every sales and operations plan lies in the accuracy of the baseline forecast. John Galt has compiled a list of business and sales forecasting techniques that addresses many of the business issues facing companies today. In 1998, we entered our algorithms in the M3 competition, an academic competition for statistical accuracy, and ranked #1 and #2 in most categories.
Our sales forecasting software and methods & statistics have been integrated into Peoplesoft’s Supply Chain Solutions and GEAC’s Comshare budgeting software packages. Our ForecastX symbol stands for quality and reliability. If you are interested in integrating our forecasting algorithms into your own application or commercial forecasting software, read more about the ForecastX SDK. This business and sales forecasting software includes more than 20 forecasting techniques and more than 40 statistical forecasting methods & statistics.
Forecast Model Selection
Accuracy Statistics
- Theil’s Statistic
- Sum Squared Error (SSE)
- Standard Deviation of Error
- Root Mean Square Error (RMSE)
- R-Squared
Business Modeling
Causal Forecasting
- Stepwise Regression with Dynamic Lagging
- Multiple Regression
- Polynomial Regression
- Linear Regression
Descriptive Statistics
Distributions
Expert Selection
Multi-Level Grouping
New Products
- New Product Forecasting
- Gompertz Curve
- Logistic Curve
- Probit Curve
- Link Seasonal Patterns & Replacement Products
Seasonal Models
- Holt’s Winters Exponential Smoothing (Additive and Multiplicative)
- Decomposition (Additive and Multiplicative)
- Census II X-11 (Additive and Multiplicative)
- Box-Jenkins (ARIMA Modeling)
Simple Methods (Time Series)
- Time Series Explained
- Moving Average
- Weighted Moving Average
- Simple Exponential Smoothing
- Holt’s Double Exponential Smoothing
- Brown’s Triple Exponential Smoothing
