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Version 0.1.7

• Replacing deprecated functions:
  • 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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.