| Title: | Simulate complex synthetic time series for benchmarks |
|---|---|
| Description: | Simulate complex synthetic time series for benchmarks. |
| Authors: | T. Moudiki |
| Maintainer: | T. Moudiki <[email protected]> |
| License: | MIT |
| Version: | 0.2.0 |
| Built: | 2024-11-01 11:28:50 UTC |
| Source: | https://github.com/thierrymoudiki/simulatetimeseries |
Data from Task Views + synthetic
get_data_1(diffs = TRUE)get_data_1(diffs = TRUE)
diffs |
return the differentiated series or not? (lag = 1) |
a list of time series objects
Simulate a univariate time series dataset 1
simulate_time_series_1( n, trend = c("linear", "quadratic"), seasonality = c("none", "sinusoidal"), distribution = c("normal", "student"), noise_sd = 10, seed = 123 )simulate_time_series_1( n, trend = c("linear", "quadratic"), seasonality = c("none", "sinusoidal"), distribution = c("normal", "student"), noise_sd = 10, seed = 123 )
n |
numerical, number of data points |
trend |
string, "linear" or "quadratic" |
seasonality |
string, "none" or "sinusoidal" |
distribution |
string, "normal" and "student" |
noise_sd |
numerical, standard deviation of noise |
seed |
int, reproducibility seed |
a native time series object
ts_data <- simulate_time_series_1( n = 100L, trend = "quadratic", seasonality = "sinusoidal", noise_sd = 2500, distribution = "normal" ) plot(ts_data, type = "l", main = "Simulated Time Series")ts_data <- simulate_time_series_1( n = 100L, trend = "quadratic", seasonality = "sinusoidal", noise_sd = 2500, distribution = "normal" ) plot(ts_data, type = "l", main = "Simulated Time Series")
Simulate a univariate time series dataset 2
simulate_time_series_2( n, trend = c("linear", "sinusoidal"), seasonality = FALSE, noise_sd = 0.1, ar = 0, ma = 0, seed = 123 )simulate_time_series_2( n, trend = c("linear", "sinusoidal"), seasonality = FALSE, noise_sd = 0.1, ar = 0, ma = 0, seed = 123 )
n |
numerical, number of data points |
trend |
string, "linear" or "sinusoidal" |
seasonality |
string, "none" or "sinusoidal" |
noise_sd |
numerical, standard deviation of noise |
ar |
autoregressive order |
ma |
moving average order |
seed |
int, reproducibility seed |
a native time series object
ts_data <- simulate_time_series_2( n = 100L, trend = "sinusoidal", seasonality = TRUE, noise_sd = runif(n = 1, min = 20, max=50) ) plot(ts_data, type = "l", main = "Simulated Time Series")ts_data <- simulate_time_series_2( n = 100L, trend = "sinusoidal", seasonality = TRUE, noise_sd = runif(n = 1, min = 20, max=50) ) plot(ts_data, type = "l", main = "Simulated Time Series")
Simulate a univariate time series dataset 3
simulate_time_series_3(n = 100, seed = 123)simulate_time_series_3(n = 100, seed = 123)
n |
numerical, number of data points |
seed |
int, reproducibility seed |
a native time series object
print(simulate_time_series_3(10))print(simulate_time_series_3(10))
Simulate a univariate time series dataset 4
simulate_time_series_4(n = 600, psi = 0.1, theta = 0.1, seed = 123)simulate_time_series_4(n = 600, psi = 0.1, theta = 0.1, seed = 123)
n |
numerical, number of data points |
psi |
1st parameter for innovation variance (in [0, 1]) |
theta |
2nd parameter for innovation variance (in [0, 1]) |
seed |
int, reproducibility seed |
a native time series object
plot(simulate_time_series_4())plot(simulate_time_series_4())