ts_split - replacing the is.tsibble
function with is_tsibble function
ts_grid - replacing the future package lapply function
with the parallel package implementation
Version 0.1.6
Fixing errors on the train_model function:
Error with the forecast output
Error with the nnetar model
Replacing the xts::indexClass function with
xts::tclass function
Removing the ts_backtesting function, which was
replaced by the train_model function
Removing the ts_acf and ts_pacf functions,
the ts_cor will replace them
Removing the bsts package from the package
dependency
Version 0.1.5
Package license
Changing the package license from GPL-3 to MIT
New functions
train_model - a flexible framework for training, testing,
evaluating, and forecasting models. This function provides the ability
to run multiple models with backtesting or single training/testing
partitions
plot_model - animation the performance of the train_model output on
the backtesting partitions
plot_error - plotting the error distribution of the train_model
output
ts_cor - for acf and pacf plots with seasonal lags
arima_diag - a diagnostic plot for identify the AR, MA and
differencing components of the ARIMA model
Deprecated functions
ts_backtesting - will be replaced by the train_model function
ts_acf / ts_pacf functions - will be replaced by the ts_cor
function
Fix errors
ts_seasonal - aligning the box plot color
ts_plot - setting the dash and marker mode for multiple time
series
Version 0.1.4
New functions
forecast_sim - creating different forecast paths for forecast
objects (when applicable), by utilizing the underline model distribution
with the simulate function
ts_grid - tuning time series models with grid search approach using
backtesting method. Currently, support only the Holt-Winters model
plot_grid - plotting the output of the ts_grid function
Fix errors
ts_plot, test_forecast - avoid default setting of the plot_ly
function, and set explicitly the plot setting (e.g., color, line mode,
etc.). This allows using the function with the plotly subplot
function
ts_seasonal - define the order of the frequency units of the box
plot option plot_forecast - fixing a gap between the forecast values and
the time (x-axis) values
Version 0.1.3
ts_to_prophet function for converting ts objects (“ts”, “zoo” and
“xts” class) to prophet object
ccf_plot function for plotting corss correlation lags between two
time series
Fixed error in the ts_backtesting function - supprting xreg
option
Version 0.1.2
New functions
ts_backtesting - a horce race of multiple forecasting models with
backtesting
ts_quantile - time series quantile plot for time series data
ts_seasonal - supports multiple inputs and new color palattes
Version 0.1.1
New functions
New options for the seasonality plot
Heatmap and surface plots
Polar plot
Converting function from xts and zoo to ts class
Spliting function for ts object for training and testing
partitions
Time series lags plot - ts_lags() function
Function ts_split() to split ‘ts’ object into training and testing
partitions
Functions for converting xts and zoo objects for ts object:
xts_to_ts(), and
zoo_to_ts()
Two types for the seasonal_ly() plot:
“normal” - seasonal variation by year, or
“cycle” - seasonal variation by the cycle units over time (months or
quarters)
“polar” - polar plot for seasonality
“box” - box-plot by cycle units
Decompose plot with the decompose_ly() function
Data set - US monthly total vehicle sales: 1976 - 2017 (USVSales),
‘ts’ object
Data set - US monthly civilian unemployment rate: 1948 - 2017
(USUnRate), ‘ts’ object
Data set - US monthly natural gas consumption: 2000 - 2017 (USgas),
‘ts’ object
Data set - University of Michigan Consumer Survey, Index of Consumer
Sentiment: 1980 - 2017 (Michigan_CS), ‘xts’ object
Data set - Monthly crude Oil Prices: Brent - Europe: 1987 - 2017
(EURO_Brent), ‘zoo’ object
Version 0.1.0
Function for plotting univariate and multivariate time series
data
Evaluation plot for the testing set (hold-out data)
Interactive seasonality plot
Functions for interactive plot for the ACF and PACF