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How do you determine how many observations to average in a moving average model?
Wiki User
∙ 10y ago
The period value determines how many observations to average in a moving average model. Moving average is not a real piece of data but a comparison for forecast and valuation.
Wiki User
∙ 10y ago
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What is the meaning of arima?
Arima can be defined as an autoregressive integrated moving average (ARIMA) model is a generalization of an autoregressive moving average (ARMA) model. There models are fitted to time series data either to better understand the data and to predict future points in the series of forecasting
What is a good scientific reason to accept a model?
The model explains observations well
Why have scientist change their ideas on the model of the Atom?
There has been more and more observations about the atom that has changed it since then. They revised it to explain the observations.
How does the Ptolemaic model differ from the modern day model?
The Ptolemaic model has all of the planets moons and stars moving around the Sun. The modern day Copernican model has all the planets moving around the Sun, with the moons moving around the planets, and the Sun and its "system" moving around the Milky Way.
What model is used to determine molecular shape?
VSEPR model
What is Box Jenkins Model?
Box-Jenkins Approach The Box-Jenkins ARMA model is a combination of the AR and MA models where the terms in the equation have the same meaning as given for the AR and MA model. Comments on Box-Jenkins Model A couple of notes on this model. # The Box-Jenkins model assumes that the time series is stationary. Box and Jenkins recommend differencing non-stationary series one or more times to achieve stationarity. Doing so produces an ARIMA model, with the "I" standing for "Integrated". # Some formulations transform the series by subtracting the mean of the series from each data point. This yields a series with a mean of zero. Whether you need to do this or not is dependent on the software you use to estimate the model. # Box-Jenkins models can be extended to include seasonal autoregressive and seasonal moving average terms. Although this complicates the notation and mathematics of the model, the underlying concepts for seasonal autoregressive and seasonal moving average terms are similar to the non-seasonal autoregressive and moving average terms. # The most general Box-Jenkins model includes difference operators, autoregressive terms, moving average terms, seasonal difference operators, seasonal autoregressive terms, and seasonal moving average terms. As with modeling in general, however, only necessary terms should be included in the model. Those interested in the mathematical details can consult Box, Jenkins and Reisel (1994), Chatfield (1996), or Brockwell and Davis (2002). Stages in Box-Jenkins Modeling There are three primary stages in building a Box-Jenkins time series model. # Model Identification # Model Estimation # Model Validation RemarksThe following remarks regarding Box-Jenkins models should be noted. # Box-Jenkins models are quite flexible due to the inclusion of both autoregressive and moving average terms. # Based on the Wold decomposition thereom (not discussed in the Handbook), a stationary process can be approximated by an ARMA model. In practice, finding that approximation may not be easy. # Chatfield (1996) recommends decomposition methods for series in which the trend and seasonal components are dominant. # Building good ARIMA models generally requires more experience than commonly used statistical methods such as regression. Sufficiently Long Series RequiredTypically, effective fitting of Box-Jenkins models requires at least a moderately long series. Chatfield (1996) recommends at least 50 observations. Many others would recommend at least 100 observations. source: http://www.itl.nist.gov/div898/handbook/pmc/section4/pmc445.htm
What is Box-Jenkins Model?
Box-Jenkins Approach The Box-Jenkins ARMA model is a combination of the AR and MA models where the terms in the equation have the same meaning as given for the AR and MA model. Comments on Box-Jenkins Model A couple of notes on this model. # The Box-Jenkins model assumes that the time series is stationary. Box and Jenkins recommend differencing non-stationary series one or more times to achieve stationarity. Doing so produces an ARIMA model, with the "I" standing for "Integrated". # Some formulations transform the series by subtracting the mean of the series from each data point. This yields a series with a mean of zero. Whether you need to do this or not is dependent on the software you use to estimate the model. # Box-Jenkins models can be extended to include seasonal autoregressive and seasonal moving average terms. Although this complicates the notation and mathematics of the model, the underlying concepts for seasonal autoregressive and seasonal moving average terms are similar to the non-seasonal autoregressive and moving average terms. # The most general Box-Jenkins model includes difference operators, autoregressive terms, moving average terms, seasonal difference operators, seasonal autoregressive terms, and seasonal moving average terms. As with modeling in general, however, only necessary terms should be included in the model. Those interested in the mathematical details can consult Box, Jenkins and Reisel (1994), Chatfield (1996), or Brockwell and Davis (2002). Stages in Box-Jenkins Modeling There are three primary stages in building a Box-Jenkins time series model. # Model Identification # Model Estimation # Model Validation RemarksThe following remarks regarding Box-Jenkins models should be noted. # Box-Jenkins models are quite flexible due to the inclusion of both autoregressive and moving average terms. # Based on the Wold decomposition thereom (not discussed in the Handbook), a stationary process can be approximated by an ARMA model. In practice, finding that approximation may not be easy. # Chatfield (1996) recommends decomposition methods for series in which the trend and seasonal components are dominant. # Building good ARIMA models generally requires more experience than commonly used statistical methods such as regression. Sufficiently Long Series RequiredTypically, effective fitting of Box-Jenkins models requires at least a moderately long series. Chatfield (1996) recommends at least 50 observations. Many others would recommend at least 100 observations. source: http://www.itl.nist.gov/div898/handbook/pmc/section4/pmc445.htm
Why was Galileo model accepted?
Because it was demonstrably the best explanation for the observations that could be made.
How observations become a scientific law?
Observations form the basis of hypothesis, Mathematical modelling builds a therory based on the hypothesis. Proof of the validity of the model forms the law.
Why have scientists revised the a atomic model many times?
they could discover a new atom and it would change
How tal is an average model?
The average female fashion model is around 5'10". The average fashion male model is about 6'0".
how organized way to collect and record scientific observations is with an?
Data tabledata table...i think
