| Title: | Complete Change Point Analysis |
|---|---|
| Description: | Provides a comprehensive approach for identifying and estimating change points in multivariate time series through various statistical methods. Implements the multiple change point detection methodology from Ryan & Killick (2023) <doi:10.1080/00401706.2023.2183261> and a novel estimation methodology from Fotopoulos et al. (2023) <doi:10.1007/s00362-023-01495-0> generalized to fit the detection methodologies. Performs both detection and estimation of change points, providing visualization and summary information of the estimation process for each detected change point. |
| Authors: | Vasileios Pavlopoulos [cre, aut], Hieu Pham [aut, ctb], Paras Bhatt [aut, ctb], Yi Tan [aut, ctb], Ravi Patnayakuni [aut, ctb] |
| Maintainer: | Vasileios Pavlopoulos <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.2 |
| Built: | 2024-12-21 06:48:35 UTC |
| Source: | CRAN |
Adjusted ratio binary segmentation.
adjusted_ratio_bin_seg(input_data, minseglen, alpha)adjusted_ratio_bin_seg(input_data, minseglen, alpha)
input_data |
A numeric matrix of observations for multivariate time series data where the dimension is not greater than the observations. Date columns should not be inputted. |
minseglen |
Minimum segment length for detecting change points. |
alpha |
Level of significance for calculating the confidence intervals. |
A list with change points and segments.
# Example usage data <- matrix(rnorm(1000), ncol = 10) result <- adjusted_ratio_bin_seg(data, minseglen = 30, alpha = 0.05)# Example usage data <- matrix(rnorm(1000), ncol = 10) result <- adjusted_ratio_bin_seg(data, minseglen = 30, alpha = 0.05)
Detect and estimate change points.
decp( input_data, alpha = 0.05, num_simulations = 10000, num_iterations = 100, verbose = TRUE )decp( input_data, alpha = 0.05, num_simulations = 10000, num_iterations = 100, verbose = TRUE )
input_data |
A numeric matrix of observations for multivariate time series data where the dimension is not greater than the observations. Date columns should not be inputted. |
alpha |
Level of significance for calculating the confidence intervals |
num_simulations |
Specifies the number of simulations to be conducted during the estimation process. It is recommended to set num_simulations to a large value to ensure greater certainty and reliability of the results. A higher number of simulations helps in capturing the variability and improves the accuracy of the estimation. |
num_iterations |
Determines the size of the two-sided random walk in the estimation process (each path). If the jump size of the change point is small, num_iterations should be set to higher values to achieve accurate results. For jump size >= 1, the default value is 100. |
verbose |
Logical value indicating whether to print messages during the function execution. Default is TRUE. |
An object of class 'decp_result' containing the ordered change points, the summary of the jump sizes for each pair of segments, the Confidence Interval (C.I.) of each detected change point, the maximum zhta, confidence interval level, and warnings in case that the C.I. of two adjacent change points overlap.
# Example usage data_part1 <- matrix(rnorm(1500, mean = 0, sd = 1), ncol = 5) data_part2 <- matrix(rnorm(1500, mean = 3, sd = 1), ncol = 5) data <- rbind(data_part1, data_part2) result <- decp(data, alpha = 0.05, num_simulations = 100, num_iterations = 50) print(result)# Example usage data_part1 <- matrix(rnorm(1500, mean = 0, sd = 1), ncol = 5) data_part2 <- matrix(rnorm(1500, mean = 3, sd = 1), ncol = 5) data <- rbind(data_part1, data_part2) result <- decp(data, alpha = 0.05, num_simulations = 100, num_iterations = 50) print(result)
Maximum likelihood estimation change point detection.
mle_change_point_detection(input_data, verbose = TRUE)mle_change_point_detection(input_data, verbose = TRUE)
input_data |
A numeric matrix of observations for multivariate time series data where the dimension is not greater than the observations. Date columns should not be inputted. |
verbose |
Logical value indicating whether to print messages during the function execution. Default is TRUE. |
An object of class 'mle_change_point_result' containing the index of the change point estimate, its MLE value, and the MLE data.
# Example usage data <- matrix(rnorm(1000), ncol = 10) tau_range <- 30:(nrow(data) - 30) result <- mle_change_point_detection(data) print(result)# Example usage data <- matrix(rnorm(1000), ncol = 10) tau_range <- 30:(nrow(data) - 30) result <- mle_change_point_detection(data) print(result)
Plot method for decp_result
## S3 method for class 'decp_result' plot(x, ...)## S3 method for class 'decp_result' plot(x, ...)
x |
An object of class 'decp_result' |
... |
Additional arguments passed to the plotting function |
Plot method for mle_change_point_result
## S3 method for class 'mle_change_point_result' plot(x, ...)## S3 method for class 'mle_change_point_result' plot(x, ...)
x |
An object of class 'mle_change_point_result' |
... |
Additional arguments passed to the plotting function |
Print method for decp_result
## S3 method for class 'decp_result' print(x, ...)## S3 method for class 'decp_result' print(x, ...)
x |
An object of class 'decp_result' |
... |
Additional arguments (not used) |
Print method for 'mle_change_point_result' class
## S3 method for class 'mle_change_point_result' print(x, ...)## S3 method for class 'mle_change_point_result' print(x, ...)
x |
An object of class 'mle_change_point_result'. |
... |
Additional arguments (not used). |
The estimation of the detected change point.
simulate_estimation( lambda1, lambda2, term1, term2, num_simulations, num_iterations )simulate_estimation( lambda1, lambda2, term1, term2, num_simulations, num_iterations )
lambda1 |
Eigenvalues of the first segment. |
lambda2 |
Eigenvalues of the second segment. |
term1 |
The negative drift term of the left hand side of the random walk. |
term2 |
The negative drift term of the right hand side of the random walk. |
num_simulations |
Specifies the number of simulations to be conducted during the estimation process. It is recommended to set num_simulations to a large value to ensure greater certainty and reliability of the results. A higher number of simulations helps in capturing the variability and improves the accuracy of the estimation. |
num_iterations |
Determines the size of the two-sided random walk in the estimation process (each path). If the jump size of the change point is small, num_iterations should be set to higher values to achieve accurate results. For jump size >= 1, the default value is 100. |
A numeric vector of the estimation results centered around zero. The spike of the histogram is represents estimated change point, and it is expected to be at zero.
# Example usage lambda1 <- rnorm(10) lambda2 <- rnorm(10) term1 <- -1 term2 <- -2 result <- simulate_estimation(lambda1, lambda2, term1, term2, num_iterations = 100, num_simulations = 100)# Example usage lambda1 <- rnorm(10) lambda2 <- rnorm(10) term1 <- -1 term2 <- -2 result <- simulate_estimation(lambda1, lambda2, term1, term2, num_iterations = 100, num_simulations = 100)
Summary method for decp_result
## S3 method for class 'decp_result' summary(object, ...)## S3 method for class 'decp_result' summary(object, ...)
object |
An object of class 'decp_result' |
... |
Additional arguments (not used) |