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]

⎘ Copy

👎

Formula

✖

Single Exponential Smoothing

Formula

`"Ft" = "α"*"D"_{"t-1"}+(1-"α")*"F"_{"t-1"}`

Example

`"40"="0.2"*"44"+(1-"0.2")*"39"`

Calculator

LaTeX

Reset

👍

Download Industrial Engineering Formula PDF PDF Image

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)

You are here -

Home » Engineering » Mechanical » Industrial Engineering » Operational and Financial Factors » Single Exponential Smoothing

Credits

Created by Team Softusvista

Softusvista Office (Pune), India

Team Softusvista has created this Calculator and 600+ more calculators!

Verified by Himanshi Sharma

Bhilai Institute of Technology (BIT), Raipur

Himanshi Sharma has verified this Calculator and 800+ more calculators!

< 13 Operational and Financial Factors Calculators

Single Exponential Smoothing

Go Smooth_Averaged_Forecast_for_Period_t = Smoothing Constant*Previous observed Value+(1-Smoothing Constant)*Previous Period Forecast

New Number in Simplex Table

Go New Number of Simplex Table = Old Number of Simplex Table-Key Row of Simplex*Key Column of Simplex/Key Number of Simplex

Expected Number of Customers in Queue

Go Expected Number of Customers in Queue = (Mean_Arrival_Rate^2)/(Mean_Service_Rate*(Mean_Service_Rate-Mean_Arrival_Rate))

Probability of Customers Exceeding Number

Go Probability of Customers Exceeding Number = Mean_Arrival_Rate*Exceeded Number Queuing Theory/Mean_Service_Rate

Number of Kanbans

Go No. of Kanban = (Demand_per_Year*Lead Time*(1+Safety_Factor))/Container Size

Expected Number of Customers in System

Go Expected Number of Customers in System = Mean_Arrival_Rate/(Mean_Service_Rate-Mean_Arrival_Rate)

Expected Length of Non-Empty Queue

Go Expected Length of Non-empty Queue = Mean_Service_Rate/(Mean_Service_Rate-Mean_Arrival_Rate)

Gross Margin Return on Investment

Go Return_on_Investment_(ROI) = Gross_Profit/((Opening Stock-Closing Stock)/2)*100

Perfect Order Measurement

Go Perfect Order Measurement = ((Total Orders-Error Orders)/Total Orders)*100

Uniform Series Present Sum of Money

Go Annual_Devaluation_Rate = Rate_of_Return_Foreign_Currency+Rate_of_Return_USD

Point r on Line

Go Point r on Line = Point a+Lambda*Point b

Non-Empty Queue Probability

Go Non-empty Queue Probability = (Mean_Arrival_Rate/Mean_Service_Rate)^2

Standard Error (Pooled)

Go Standard Error = (Mean Square Error^0.5)/Observations

Single Exponential Smoothing Formula

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.

Home

FREE PDFs

🔍 Search