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

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