Statistical Learning for Big Dependent Data
Find Outliers Using an Upper and a Lower Timewise Quantile Series
Outliers LASSO
Plot the Closest Series to a Given Timewise Quantile Series
Rank Individual Time Series According to a Given Timewise Quantile Ser...
Select the Penalty Parameter of LASSO-type Linear Regression
Plot Multiple Time Series in One Frame
Plot Timewise Quantiles in One Frame
Automatic Modeling of a Scalar Time Series
Automatic Modeling of a Scalar Non-Seasonal Time Series
Check the Seasonality of Each Component of a Multiple Time Series
Check for Possible Non-linear Transformations of a Multiple Time Serie...
Cluster Identification Procedure using Projections on Directions of Ex...
Dynamic Factor Model by Principal Components
Create an input data matrix for a Deep learning program that uses time...
Random Draw of Coefficients for AR Models and MA Models
Plot the Observed Time Series and Selected EDQs (Empirical Dynamic Qua...
Empirical Dynamic Quantile for Visualization of High-Dimensional Time ...
Gap statistics
Clustering of Time Series Using the Generalized Cross Correlation Meas...
Generalized Cross-Correlation Matrix
Plot a Selected Time Series Using Quantile as the Background
Multivariate Outlier Detection
Quantile Boxplot
Setup the Input and Output for a Recurrent Neural Network
Automatic Modeling of a Scalar Seasonal Time Series
Scatterplot of Two Selected-lag Autocorrelation Functions
Identified the Series with the Given Order
Find the Number of Clusters by the Standard Silhouette Statistics
Generate Unit-root ARIMA Possibly Seasonal Time Series
Selected Seasonal Time Series
Collects All Models Specified by "sarimaSpec"
Stepp
Summary Statistics of Cross-Correlation Matrices
Collects All Models Specified by "arimaSpec"
Summary Outliers
Boxplots of the Medians of Subperiods
Boosting with Simple Linear Regression
Programs for analyzing large-scale time series data. They include functions for automatic specification and estimation of univariate time series, for clustering time series, for multivariate outlier detections, for quantile plotting of many time series, for dynamic factor models and for creating input data for deep learning programs. Examples of using the package can be found in the Wiley book 'Statistical Learning with Big Dependent Data' by Daniel Peña and Ruey S. Tsay (2021). ISBN 9781119417385.