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Tags

#m249 #mathematics #open-university #statistics #time-series

Question

The 1-step ahead forecast error at time t, which is denoted e_{t}, is the diﬀerence between the observed value and the 1-step ahead forecast of X_{t}:

e_{t }= x_{t} - \(\hat{x}_t\)

The sum of squared errors, or SSE, is given by

SSE = [...]

Given observed values x_{1} ,x_{2} ,...,x_{n} ,the optimal value of the smoothing parameter α for simple exponential smoothing is the value that minimizes the sum of squared errors.

e

The sum of squared errors, or SSE, is given by

SSE = [...]

Given observed values x

Answer

\(\large SSE = \sum_{t=1}^ne_t^2 = \sum_{t=1}^n(x_t-\hat{x}_t)^2\)

Tags

#m249 #mathematics #open-university #statistics #time-series

Question

The 1-step ahead forecast error at time t, which is denoted e_{t}, is the diﬀerence between the observed value and the 1-step ahead forecast of X_{t}:

e_{t }= x_{t} - \(\hat{x}_t\)

The sum of squared errors, or SSE, is given by

SSE = [...]

Given observed values x_{1} ,x_{2} ,...,x_{n} ,the optimal value of the smoothing parameter α for simple exponential smoothing is the value that minimizes the sum of squared errors.

e

The sum of squared errors, or SSE, is given by

SSE = [...]

Given observed values x

Answer

?

Tags

#m249 #mathematics #open-university #statistics #time-series

Question

The 1-step ahead forecast error at time t, which is denoted e_{t}, is the diﬀerence between the observed value and the 1-step ahead forecast of X_{t}:

e_{t }= x_{t} - \(\hat{x}_t\)

The sum of squared errors, or SSE, is given by

SSE = [...]

Given observed values x_{1} ,x_{2} ,...,x_{n} ,the optimal value of the smoothing parameter α for simple exponential smoothing is the value that minimizes the sum of squared errors.

e

The sum of squared errors, or SSE, is given by

SSE = [...]

Given observed values x

Answer

\(\large SSE = \sum_{t=1}^ne_t^2 = \sum_{t=1}^n(x_t-\hat{x}_t)^2\)

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step ahead forecast error at time t, which is denoted e t , is the diﬀerence between the observed value and the 1-step ahead forecast of X t : e t = x t - \(\hat{x}_t\) The sum of squared errors, or SSE, is given by SSE <span>= \(\large \sum_{t=t}^ne_t^2 = \sum_{t=t}^n(x_t-\hat{x}_t)^2\) Given observed values x 1 ,x 2 ,...,x n ,the optimal value of the smoothing parameter α for simple exponential smoothing is the value that minimizes the sum of squared errors.<

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step ahead forecast error at time t, which is denoted e t , is the diﬀerence between the observed value and the 1-step ahead forecast of X t : e t = x t - \(\hat{x}_t\) The sum of squared errors, or SSE, is given by SSE <span>= \(\large \sum_{t=t}^ne_t^2 = \sum_{t=t}^n(x_t-\hat{x}_t)^2\) Given observed values x 1 ,x 2 ,...,x n ,the optimal value of the smoothing parameter α for simple exponential smoothing is the value that minimizes the sum of squared errors.<

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

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