Tags
#m249 #mathematics #open-university #statistics #time-series
Question
If a time series Xt is described by an additive model with constant level and no seasonality, 1-step ahead forecasts may be obtained by simple exponential smoothing using the formula
$$\hat{x}_{n+1}$$ = αxn + (1 − α)$$\hat{x}_n$$
where:
• xn is the observed value at time n,
• $$\hat{x}_n$$​and $$\hat{x}_{n+1}$$are the 1-step ahead forecasts of Xn and Xn+1,
• and α is [...].
The method requires an initial value $$\hat{x}_1$$, which is often chosen to be x1 : $$\hat{x}_1$$ = x1.
a smoothing parameter, 0 ≤ α ≤ 1

Tags
#m249 #mathematics #open-university #statistics #time-series
Question
If a time series Xt is described by an additive model with constant level and no seasonality, 1-step ahead forecasts may be obtained by simple exponential smoothing using the formula
$$\hat{x}_{n+1}$$ = αxn + (1 − α)$$\hat{x}_n$$
where:
• xn is the observed value at time n,
• $$\hat{x}_n$$​and $$\hat{x}_{n+1}$$are the 1-step ahead forecasts of Xn and Xn+1,
• and α is [...].
The method requires an initial value $$\hat{x}_1$$, which is often chosen to be x1 : $$\hat{x}_1$$ = x1.
?

Tags
#m249 #mathematics #open-university #statistics #time-series
Question
If a time series Xt is described by an additive model with constant level and no seasonality, 1-step ahead forecasts may be obtained by simple exponential smoothing using the formula
$$\hat{x}_{n+1}$$ = αxn + (1 − α)$$\hat{x}_n$$
where:
• xn is the observed value at time n,
• $$\hat{x}_n$$​and $$\hat{x}_{n+1}$$are the 1-step ahead forecasts of Xn and Xn+1,
• and α is [...].
The method requires an initial value $$\hat{x}_1$$, which is often chosen to be x1 : $$\hat{x}_1$$ = x1.
a smoothing parameter, 0 ≤ α ≤ 1
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e obtained by simple exponential smoothing using the formula x n+1 = αx n + (1 − α)\hat{x}_n where: x n is the observed value at time n, \hat{x}_n​and \hat{x}_{n+1}are the 1-step ahead forecasts of X n and X n+1 , and α is <span>a smoothing parameter, 0 ≤ α ≤ 1. The method requires an initial value \hat{x}_1, which is often chosen to be x 1 : \hat{x}_1 = x 1 .<span><body><html>

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