If a time series Xt is described by an additive model with [...]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 a smoothing parameter, 0 ≤ α ≤ 1.
The method requires an initial value \(\hat{x}_1\), which is often chosen to be x1 : \(\hat{x}_1\) = x1.
If a time series Xt is described by an additive model with [...]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 a smoothing parameter, 0 ≤ α ≤ 1.
The method requires an initial value \(\hat{x}_1\), which is often chosen to be x1 : \(\hat{x}_1\) = x1.
If a time series Xt is described by an additive model with [...]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 a smoothing parameter, 0 ≤ α ≤ 1.
The method requires an initial value \(\hat{x}_1\), which is often chosen to be x1 : \(\hat{x}_1\) = x1.
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Open it If a time series X t 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
x n+1 = αx n + (1 − α)\hat{x}_n
where:
x n is the ob
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