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What is the definition of non collinear?
it means a series of points that are not on the same line in a plane.
Can you top swap a non supercharged 3800 series 3 with a 3800 series 3 supercharged?
yes. but its easier said than done.
Do Time series methods base forecasts only on past values of the variables?
They make forecasts based on both present and past values of the variables. Stated in non-technical terms, they assume that somehow history repeats itself, i.e. that some patterns in the time series behavior of data are recurrent. As a consequence, the past is useful to predict the future.
What is the difference between polynomial and non polynomial time complexity?
Polynomial vs non polynomial time complexity
What is linear model communication?
non-linear model of communication is a way of communication that is thoght to came from the creative side of the brain that gets the message across in a round about way
What is non-locomotor?
non locomotor is the movement is stationary
What means non-stationary?
mobile
What was movement in Norway?
Non stationary objects.
what is locomotor?
non locomotor is the movement is stationary
Can a stationary object have a non zero angular acceleration?
no
What are the components of a sales system?
conceptual,empirical,natural,artificial,social,machine,open,close,adaptive,non adaptive,probabilitic,deterministic,permanent,temporary,stationary,non stationary
What is a non stationary signal?
A non-stationary signal is one whose frequency changes over time; e.g. human speech where frequencies vary over time depending on what words or syllables you are pronouncing. On the contrary, you have stationary signals where frequencies don't change over time; e.g. the signal: cos(20*pi*t)+cos(50*pi*t)+cos(200*pi*t) where all of the frequency components (20*pi, 50*pi, 200*pi) exist at all times.
What is a non-stationary signal?
A non-stationary signal is one whose frequency changes over time; e.g. human speech where frequencies vary over time depending on what words or syllables you are pronouncing. On the contrary, you have stationary signals where frequencies don't change over time; e.g. the signal: cos(20*pi*t)+cos(50*pi*t)+cos(200*pi*t) where all of the frequency components (20*pi, 50*pi, 200*pi) exist at all times.
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
How do you determine wheather your 87 Toyota supra non turbo 3.0 is a gt or gte model engine series?
Non turbos are GT, turbos are GTE. Since yours is non turbo, that makes it a GT.
What has the author Mordechai Segal written?
Mordechai Segal has written: 'Optimal source localization and tracking using arrays with uncertainties in sensor locations' -- subject(s): Array processors 'Time delay estimation in stationary and non-stationary environments' -- subject(s): Array processors
