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Single Exponential Smoothing Formula

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Smooth_Averaged_Forecast_for_Period_t is the recent observation that is given relatively more weight in forecasting than the older observations. Check FAQs

Ft = α \cdot D t-1 + (1 - α) \cdot F t-1

Ft - Smooth_Averaged_Forecast_for_Period_t?α - Smoothing Constant?D_t-1 - Previous observed Value?F_t-1 - Previous Period Forecast?

Single Exponential Smoothing Example

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Here is how the Single Exponential Smoothing equation looks like with Values.

Here is how the Single Exponential Smoothing equation looks like with Units.

Here is how the Single Exponential Smoothing equation looks like.

40 = 0.2 \cdot 44 + (1 - 0.2) \cdot 39

40 = 0.2 \cdot 44 + (1 - 0.2) \cdot 39

40 = 0.2 \cdot 44 + (1 - 0.2) \cdot 39

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Single Exponential Smoothing Solution

Follow our step by step solution on how to calculate Single Exponential Smoothing?

FIRST Step Consider the formula

Ft = α \cdot D t-1 + (1 - α) \cdot F t-1

Next Step Substitute values of Variables

∴

Ft = 0.2 \cdot 44 + (1 - 0.2) \cdot 39

Next Step Prepare to Evaluate

∴

Ft = 0.2 \cdot 44 + (1 - 0.2) \cdot 39

LAST Step Evaluate

∴

Ft = 40

Single Exponential Smoothing Formula Elements

Variables

Smooth_Averaged_Forecast_for_Period_t

Smooth_Averaged_Forecast_for_Period_t is the recent observation that is given relatively more weight in forecasting than the older observations.

Symbol: Ft

Measurement: NAUnit: Unitless

Note: Value can be positive or negative.

Formulas to find Smooth_Averaged_Forecast_for_Period_t

Smoothing Constant

A smoothing constant is a variable used in time series analysis based on exponential smoothing. The higher the smoothing constant, the greater weight assigned to the values from the latest period.

Symbol: α

Measurement: NAUnit: Unitless

Note: Value should be between 0 to 1.

Formulas to find Smoothing Constant

Previous observed Value

The Previous observed Value is the real value from data at time t-1 based on which predictions will be made.

Symbol: D_t-1

Measurement: NAUnit: Unitless

Note: Value can be positive or negative.

Formulas to find Previous observed Value

Previous Period Forecast

The Previous Period Forecast is the older observed forecasted value that is relatively less weight than the future prediction.

Symbol: F_t-1

Measurement: NAUnit: Unitless

Note: Value can be positive or negative.

Formulas to find Previous Period Forecast

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How to Evaluate Single Exponential Smoothing?

Single Exponential Smoothing evaluator uses Smooth_Averaged_Forecast_for_Period_t = Smoothing Constant*Previous observed Value+(1-Smoothing Constant)*Previous Period Forecast to evaluate the Smooth_Averaged_Forecast_for_Period_t, Single exponential smoothing is a time series forecasting method for uni variate data without a trend or seasonality. Smooth_Averaged_Forecast_for_Period_t is denoted by Ft symbol.

How to evaluate Single Exponential Smoothing using this online evaluator? To use this online evaluator for Single Exponential Smoothing, enter Smoothing Constant (α), Previous observed Value (D_t-1) & Previous Period Forecast (F_t-1) and hit the calculate button.

FAQs on Single Exponential Smoothing

What is the formula to find Single Exponential Smoothing?

The formula of Single Exponential Smoothing is expressed as Smooth_Averaged_Forecast_for_Period_t = Smoothing Constant*Previous observed Value+(1-Smoothing Constant)*Previous Period Forecast. Here is an example- 40 = 0.2*44+(1-0.2)*39.

How to calculate Single Exponential Smoothing?

With Smoothing Constant (α), Previous observed Value (D_t-1) & Previous Period Forecast (F_t-1) we can find Single Exponential Smoothing using the formula - Smooth_Averaged_Forecast_for_Period_t = Smoothing Constant*Previous observed Value+(1-Smoothing Constant)*Previous Period Forecast.

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Single Exponential Smoothing Formula

Single Exponential Smoothing Example

Single Exponential Smoothing Solution

Single Exponential Smoothing Formula Elements

Other formulas in Operational and Financial Factors category

How to Evaluate Single Exponential Smoothing?

FAQs on Single Exponential Smoothing

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