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Exponential smoothing model is
- a)a regression model of all data points
- b)a simple moving average model considering all data points
- c)a weighted average model with unequal weights to all data points
- d)a weighted average model with equal weights to all data points
Correct answer is option 'C'. Can you explain this answer?
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Verified Answer
Exponential smoothing model isa)a regression model of all data pointsb...
In exponential smoothing model, large weightage is given to the recent data and as the data become older its weightage also decreases.
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Exponential smoothing model isa)a regression model of all data pointsb...
Exponential smoothing is a forecasting technique that is commonly used in time series analysis. It is a weighted average model that assigns exponentially decreasing weights to past observations. The weights decrease exponentially as the observations get older.
Exponential smoothing can be used to forecast future values based on historical data. It is particularly useful when there is a trend and/or seasonality in the data. The technique is based on the assumption that the future values of a time series are a function of its past values.
The exponential smoothing model calculates the forecasted value (Ft) for time period t using the following equation:
Ft = α * Yt + (1-α) * Ft-1
Where:
- Ft is the forecasted value for time period t
- Yt is the actual value for time period t
- Ft-1 is the forecasted value for the previous time period (t-1)
- α is the smoothing parameter, also known as the smoothing constant or the weight of the current observation
Key points:
- Exponential smoothing is a weighted average model with unequal weights assigned to all data points.
- The weights assigned to the observations decrease exponentially as the observations get older.
- The smoothing parameter α determines the rate at which the weights decrease. A higher α gives more weight to recent observations, while a lower α gives more weight to past observations.
- The forecasted value for each time period is calculated by combining the actual value for that time period with the forecasted value for the previous time period, using the smoothing parameter α.
In summary, the exponential smoothing model is a weighted average model that assigns exponentially decreasing weights to past observations. It is a simple and effective technique for forecasting future values based on historical data.
Exponential smoothing can be used to forecast future values based on historical data. It is particularly useful when there is a trend and/or seasonality in the data. The technique is based on the assumption that the future values of a time series are a function of its past values.
The exponential smoothing model calculates the forecasted value (Ft) for time period t using the following equation:
Ft = α * Yt + (1-α) * Ft-1
Where:
- Ft is the forecasted value for time period t
- Yt is the actual value for time period t
- Ft-1 is the forecasted value for the previous time period (t-1)
- α is the smoothing parameter, also known as the smoothing constant or the weight of the current observation
Key points:
- Exponential smoothing is a weighted average model with unequal weights assigned to all data points.
- The weights assigned to the observations decrease exponentially as the observations get older.
- The smoothing parameter α determines the rate at which the weights decrease. A higher α gives more weight to recent observations, while a lower α gives more weight to past observations.
- The forecasted value for each time period is calculated by combining the actual value for that time period with the forecasted value for the previous time period, using the smoothing parameter α.
In summary, the exponential smoothing model is a weighted average model that assigns exponentially decreasing weights to past observations. It is a simple and effective technique for forecasting future values based on historical data.
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Exponential smoothing model isa)a regression model of all data pointsb)a simple moving average model considering all data pointsc)a weighted average model with unequal weights to all data pointsd)a weighted average model with equal weights to all data pointsCorrect answer is option 'C'. Can you explain this answer?
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