Best fit forecasting is a statistical method used to predict future values by selecting the most suitable forecasting model based on historical data. It involves analyzing different forecasting techniques and choosing the one that best aligns with past trends and patterns to minimize errors in future projections. The Multiple Model Evaluation considers various forecasting methods with each model assessed using error metrics like Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), or Root Mean Squared Error (RMSE). The model with the lowest error and best predictive capability is chosen for future forecasting. Best fit forecasting allows businesses to adjust models dynamically as new data becomes available, ensuring continuous accuracy. Best fit forecasting is employed in demand planning (predicting sales and inventory requirements), supply chain management (optimizing stock levels and logistics), financial forecasting (predicting revenue and expenses), and workforce planning (projecting staffing requirements).

 

Best Fit Forecasting in RightChain.ai

Best Fit Forecasting is employed in RightChain™ Forecasting, RightChain™ Insights for Inventory, RightChain™ Insights for Transportation, RightChain™ Insights for Warehousing, and RightChain™ Insights for Planning.

 

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Example application of Best Fit Forecasting in RightChain.ai

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Best Fit Forecasting Algorithms

Best-fit forecasting involves using historical data to select the most accurate statistical or AI-driven forecast model. The best-fit model minimizes prediction error, based on metrics like RMSE, MAPE, or MAE. Best Fit Forecasting employs and then chooses from the best of a variety of forecasting algorithms including Simple Moving Average (SMA), Exponential Smoothing (ES), AutoRegressive Integrated Moving Average (ARIMA), Regression Analysis, TBATS, and Neural Networks (NN). A description of each algorithm is provided in the table below.

 

Simple Moving Average (SMA)

Averages past data over a fixed period of time.

 

Exponential Smoothing (ES)

Gives more weight to recent data points.

 

AutoRegressive Integrated Moving Average (ARIMA)

Accounts for trends and seasonality.

 

Regression Analysis: 

Identifies relationships between variables.

 

TBATS
T: Trigonometric seasonal components

B: Box-Cox transformation

A: ARMA (Auto-Regressive Moving Average) errors

T: Trend component

S: Seasonal smoothing

Uses Box-Cox transformation to stabilize variance. Identifies and models multiple seasonal patterns.

Captures trends and smooths fluctuations. Captures short-term dependencies and autocorrelations.

Helps prevent runaway trends that might not persist. Uses exponential smoothing to model multiple seasonal patterns.

 

Neural Network Autoregression (NNAR)

A time series forecasting method that utilizes artificial neural networks (ANNs) to predict future values based on past observations.