Package NlinTS. September 10, 2018

Transcription

1 Type Package Title Non Linear Time Series Analysis Version Date Package NlinTS September 10, 2018 Maintainer Youssef Hmamouche Models for time series forecasting and causality detection. The main functionalities of this package consist of a neural network Vector Auto-Regressive model, the classical causality test C.W.J.Granger (1980) <doi: / (80)90069-x>, and a nonlinear version of it based on feed-forward neural networks. License GNU General Public License Depends Rcpp Imports methods, timeseries, Rdpack RdMacros Rdpack LinkingTo Rcpp SystemRequirements C++11 NeedsCompilation yes RoxygenNote Author Youssef Hmamouche [aut, cre], Sylvain Barthelemy [cph] Repository CRAN Date/Publication :30:03 UTC R topics documented: causality.test df.test nlin_causality.test varmlp Index 6 1

2 2 causality.test causality.test The Granger causality test The Granger causality test causality.test(ts1, ts2,, diff = FALSE) ts1 ts2 diff Numerical dataframe containing one variable. Numerical dataframe containing one variable. The parameter. Logical argument for the option of making data stationary before making the test. The test evaluates if the second time series causes the first one using the Granger test of causality. pvalue: the p-value of the test. Ftest: the statistic of the test. summary (): shows the test results. References Granger CWJ (1980). Testing for Causality. Journal of Economic Dynamics and Control, 2, pp ISSN , doi: / (80)90069X. library (timeseries) # to extract time series model = causality.test (data[,1], data[,2], 2) model$summary ()

3 df.test 3 df.test Augmented Dickey_Fuller test Augmented Dickey_Fuller test df.test(ts, ) ts Numerical dataframe. The parameter. Computes the stationarity test for a given univariate time series. df: returns the value of the test. summary (): shows the test results. References Elliott G, Rothenberg TJ and Stock JH (1992). Efficient tests for an autoregressive unit root. library (timeseries) #load data model = df.test (data[,1], 1) model$summary ()

4 4 nlin_causality.test nlin_causality.test A non linear Granger causality test A non linear Granger causality test nlin_causality.test(ts1, ts2,, LayersUniv, LayersBiv, iters, bias = TRUE) ts1 ts2 LayersUniv LayersBiv iters bias Numerical series. Numerical series. The parameter Integer vector that contains the size of hidden layers of the univariate model. The length of this vector is the number of hidden layers, and the i-th element is the number of neurons in the i-th hidden layer. Integer vector that contains the size of hidden layers of the bivariate model. The length of this vector is the number of hidden layers, and the i-th element is the number of neurons in the i-th hidden layer. The number of iterations. Logical argument for the option of using the bias in the networks. The test evaluates if the second time series causes the first one. Two MLP artificial neural networks are evaluated to perform the test, one using just the target time series (ts1), and the second using both time series. pvalue: the p-value of the test. Ftest: the statistic of the test. summary (): shows the test results. library (timeseries) # to extract time series # We construct the model based model = nlin_causality.test (data[,1], data[,2], 2, c(2, 2), c(4, 4), 500, TRUE) model$summary ()

5 varmlp 5 varmlp Artificial Neural Network VAR (Vector Auto-Regressive) model using a MultiLayer Perceptron. Artificial Neural Network VAR (Vector Auto-Regressive) model using a MultiLayer Perceptron. varmlp(df,, sizeofhlayers, iters, bias = TRUE) df sizeofhlayers iters bias A numerical dataframe The parameter. Integer vector that contains the size of hidden layers, where the length of this vector is the number of hidden layers, and the i-th element is the number of neurons in the i-th hidden layer. The number of iterations. Logical, true if the bias have to be used in the network. This function builds the model, and returns an object that can be used to make forecasts and can be updated using new data. train (df): updates the model using the input dataframe. forecast (df): returns the next row forecasts of an given dataframe. library (timeseries) # to extract time series #load data # Prepare data to make one forecasts train_data = head (data, nrow (data) - 1) test_data = tail (data, 1) model = varmlp (train_data, 1, c(10,5), 200, TRUE) predictions = model$forecast (train_data) print (tail (predictions,1)) # Update the model (learning from new data) model$train (test_data)

6 Index causality.test, 2 df.test, 3 nlin_causality.test, 4 varmlp, 5 6