Autoregressive-Moving-Average (ARMA) Training Algorithm

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An Autoregressive-Moving-Average (ARMA) Training Algorithm is a model-based time series fitting model that provide a parsimonious description of a (weakly) stationary stochastic process in terms of two polynomials, one for the auto-regression and the second for the moving average.



References

2018

Python Code
# ARMA example
from statsmodels.tsa.arima_model import ARMA
from random import random
# contrived dataset
data = [random() for x in range(1, 100)]
# fit model
model = ARMA(data, order=(2, 1))
model_fit = model.fit(disp=False)
# make prediction
that = model_fit.predict(len(data), len(data))
print(yhat)

2016

2015

  • http://www.mathworks.com/help/econ/arma-model.html
    • QUOTE: An ARMA model expresses the conditional mean of yt as a function of both past observations, yt−1,…,yt−p, and past innovations, εt−1,…,εt−q. The number of past observations that yt depends on, p, is the AR degree. The number of past innovations that yt depends on, q, is the MA degree. In general, these models are denoted by ARMA(p,q).

2014