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 is now available on CRAN

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 is now available on CRAN

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 is now available on CRAN

• 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 is now available on CRAN

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 is now available on CRAN

What’s new: * 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

Updates for the development version (0.1.0.9000) on Github

• 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 is now available on CRAN

• 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