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aic5()
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aic5: slightly faster model estimations
todo: rewrite backcast and aic.wge for real speed gains
returns the top 5 aic |
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aicbic()
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aicbic function
returns the top 5 aic and bic values for an ARMA time series |
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ase()
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ASE calculator (specific to tswge)
Takes the last length(xhat) entries in x and compares them to xhat |
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assess()
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assessment function!
assess a time series |
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calculate_ar1_varx()
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Computes the Population Variance of an AR(1) Time Series |
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calculate_arp_varx()
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Computes the Population Variance of an AR(p) Time Series
Note that this can also be used for AR(1) models instead of using calculate_ar1_varx
although, it will need an extra argument 'pt' |
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calculate_ts_gamma()
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Computes Gamma (Auto Covariance of a time series realization) |
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calculate_ts_gamma0()
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Computes Gamma0 (Variance of a time series realization) |
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calculate_ts_mean()
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Computes the Mean of a Time Series realization |
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calculate_ts_mean_confidence_interval()
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Computes the Confidence Interval of the Mean of a Time Series |
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calculate_ts_rho()
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Computes Rho (Auto Correlation of a time series realization) |
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calculate_ts_var_of_mean()
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Computes the Variance of the Sampling distribution of the Mean of a Time Series |
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check_stationarity()
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Wrapper to check the stationarity of a Time Series |
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compute_a()
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Given an AR(p,q) realization, this function estimates the white
noise estimates using equation 6.23 (from the text book) |
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compute_stda()
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Given the white noise estimates, this function computes
the standard deviation of the white noise. Note that non-zero
terms are removed before calcualting the variance. |
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compute_vara()
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Given the white noise estimates, this function computes
the variance of the white noise. Note that non-zero terms
are removed before calcualting the variance. |
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difference()
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Time Series transformation |
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estimate()
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paramater estimation function |
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evaluate_residuals()
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Evaluate if the dats is consistent with white noise |
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factor.wge.season()
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Prints the factor table for a pure seasonal model (p = d = q = 0) |
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fcst()
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forecast function! |
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generate()
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generator function! |
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generate_multiple_realization()
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Generate Multiple Realizations of a model
Useful for checking model appropriateness |
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get_all_a_calc()
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Given an AR(p,q) realization, this function estimates the white
noise estimates using equation 6.23 (from the text book), the
variance and the standard deviation of the white noise |
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hush()
|
Prevents inter 'cat' statememt from printing |
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lag_dfr()
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lag_dfr: Lag columns of a data frame |
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ljung_box()
|
ljung box test for white noise |
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mlag_dfr()
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mlag_dfr: Lag multiple columns of a data frame |
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ModelBuildMultivariateVAR
|
R6 class ModelBuildMultivariateVAR |
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ModelBuildNNforCaret
|
R6 class ModelBuildNNforCaret |
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ModelCombine
|
R6 class ModelCompareMultivariate |
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ModelCompareBase
|
R6 class ModelCompareBase |
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ModelCompareMultivariateVAR
|
R6 class ModelCompareMultivariate |
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ModelCompareNNforCaret
|
R6 class ModelCompareMultivariate |
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ModelCompareUnivariate
|
R6 class ModelCompareUnivariate |
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model_cor()
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Model Correlated data
Interactively build a model of correlated data |
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model_det()
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model with deterministic signal plus noise |
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model_mlr()
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Model as MLR model |
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MultivariateEDA
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R6 class MultivariateEDA |
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overfit()
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overfit function
produces an overfit table |
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phicheck()
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check if ar component is stationary (stolen from the R stats source code) |
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playground()
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Playground: generate a random time series for practice |
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plot_multiple_realizations()
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Plot multiple realizations of a model
Useful for checking model appropriateness |
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plot_res()
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plot the residuals of a time series |
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rarima()
|
radnom arima |
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rarma()
|
random arma |
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raruma()
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random aruma |
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rsigpn()
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random signal plus noise |
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sliding_ase_univariate()
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Function to calculate the sliding window ASE for a model
Supports ARMA, ARIMA, ARUMA (seasonal ARIMA) and Signal Plus Noise Models |
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sliding_ase_var()
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Function to calculate the sliding window ASE for a model
Supports VAR Model from the vars package |