Return a vector that is the predictions of a pth order Auto-Regressive (AR(p)) model.
Return a vector that is the predictions of a pth order Auto-Regressive (AR(p)) model. f_t = phi_0 * x_t-1 + ... + phi_p-1 * x_t-p + e_t
the estimated AR(p) coefficients
Apply the Durbin-Levinson Algorithm to iteratively compute the psi matrix.
Apply the Durbin-Levinson Algorithm to iteratively compute the psi matrix. The last row of the matrix gives AR coefficients.
http://www.stat.tamu.edu/~suhasini/teaching673/time_series.pdf
Estimate the coefficients for a pth order Auto-Regressive AR(p) model.
Estimate the coefficients for a pth order Auto-Regressive AR(p) model. x_t = phi_0 * x_t-1 + ... + phi_p-1 * x_t-p + e_t Uses the Durbin-Levinson Algorithm to determine the coefficients. The phi vector is pth row of psi matrix (ignoring the first (0th) column).
the order of the AR model
Estimate the coefficients for a qth order a Moving Average(q) model.
Estimate the coefficients for a qth order a Moving Average(q) model. x_t = e_t - theta_0 * e_t-1 - ... - theta_q-1 * e_t-q FIX: use Method of Innovations
Show the flaw by printing the error message.
Show the flaw by printing the error message.
the method where the error occurred
the error message
Return a vector that is the predictions of a qth order Moving Average (MA) model.
Return a vector that is the predictions of a qth order Moving Average (MA) model. x_t = e_t + theta_0 * e_t-1 + ... + theta_q-1 * e_t-q
the estimated MA(q) coefficients
Plot a function, e.
Plot a function, e.g., Auto-Correlation Function (ACF), Partial ACF (PACF).
the vector given function values
the name of the function
Given several time vectors, forecast the y-values.
For all the time points in vector t, predict the value of y = f(t) by .
Given a new discrete data vector z, predict the y-value of f(z).
Given a new discrete data vector z, predict the y-value of f(z).
the vector to use for prediction
Smooth the y vector by taking the lth order moving average.
Smooth the y vector by taking the lth order moving average.
the number of points to average
Fit an ARMA model to times series data.
The
ARMAclass provide basic time series analysis capabilities for Auto- Regressive (AR) and Moving Average (MA) models. In an 'ARMA(p, q)' model, 'p' and 'q' refer to the order of the Auto-Regressive and Moving Average components of the model. ARMA models are often used for forecasting.