Single Exponential Smoothing Calculator

✖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.ⓘ Smoothing Constant [α]			+10% -10%
✖The Previous observed Value is the real value from data at time t-1 based on which predictions will be made.ⓘ Previous Observed Value [D_t-1]			+10% -10%
✖The Previous Period Forecast is the older observed forecasted value that is relatively less weight than the future prediction.ⓘ Previous Period Forecast [F_t-1]			+10% -10%

✖Smooth_Averaged_Forecast_for_Period_t is the recent observation that is given relatively more weight in forecasting than the older observations.ⓘ Single Exponential Smoothing [Ft]

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Single Exponential Smoothing Solution

STEP 0: Pre-Calculation Summary

Formula Used

Smooth_Averaged_Forecast_for_Period_t = Smoothing Constant*Previous Observed Value+(1-Smoothing Constant)*Previous Period Forecast
Ft = α*D_t-1+(1-α)*F_t-1
This formula uses 4 Variables

Variables Used

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.
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.
Previous Observed Value - The Previous observed Value is the real value from data at time t-1 based on which predictions will be made.
Previous Period Forecast - The Previous Period Forecast is the older observed forecasted value that is relatively less weight than the future prediction.

STEP 1: Convert Input(s) to Base Unit

Smoothing Constant: 0.2 --> No Conversion Required
Previous Observed Value: 44 --> No Conversion Required
Previous Period Forecast: 39 --> No Conversion Required

STEP 2: Evaluate Formula

Substituting Input Values in Formula

Ft = α*D_t-1+(1-α)*F_t-1 --> 0.2*44+(1-0.2)*39

Evaluating ... ...

Ft = 40

STEP 3: Convert Result to Output's Unit

40 --> No Conversion Required

FINAL ANSWER

40 <-- Smooth_Averaged_Forecast_for_Period_t

(Calculation completed in 00.004 seconds)

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Single Exponential Smoothing Formula

LaTeX Go

Smooth_Averaged_Forecast_for_Period_t = Smoothing Constant*Previous Observed Value+(1-Smoothing Constant)*Previous Period Forecast
Ft = α*D_t-1+(1-α)*F_t-1

What is Single exponential smoothing?

Single Exponential Smoothing, SES for short, also called Simple Exponential Smoothing, is a time series forecasting method for univariate data without a trend or seasonality. This parameter controls the rate at which the influence of the observations at prior time steps decays exponentially.

How to Calculate Single Exponential Smoothing?

Single Exponential Smoothing calculator uses Smooth_Averaged_Forecast_for_Period_t = Smoothing Constant*Previous Observed Value+(1-Smoothing Constant)*Previous Period Forecast to calculate 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 calculate Single Exponential Smoothing using this online calculator? To use this online calculator 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. Here is how the Single Exponential Smoothing calculation can be explained with given input values -> 40 = 0.2*44+(1-0.2)*39.

FAQ

What is Single Exponential Smoothing?

Single exponential smoothing is a time series forecasting method for uni variate data without a trend or seasonality and is represented as Ft = α*D_t-1+(1-α)*F_t-1 or Smooth_Averaged_Forecast_for_Period_t = Smoothing Constant*Previous Observed Value+(1-Smoothing Constant)*Previous Period Forecast. 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, The Previous observed Value is the real value from data at time t-1 based on which predictions will be made & The Previous Period Forecast is the older observed forecasted value that is relatively less weight than the future prediction.

How to calculate Single Exponential Smoothing?

Single exponential smoothing is a time series forecasting method for uni variate data without a trend or seasonality is calculated using Smooth_Averaged_Forecast_for_Period_t = Smoothing Constant*Previous Observed Value+(1-Smoothing Constant)*Previous Period Forecast. To calculate Single Exponential Smoothing, you need Smoothing Constant (α), Previous Observed Value (D_t-1) & Previous Period Forecast (F_t-1). With our tool, you need to enter the respective value for Smoothing Constant, Previous Observed Value & Previous Period Forecast and hit the calculate button. You can also select the units (if any) for Input(s) and the Output as well.

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