• Introduction

• Key functions

• Demo

• Work on Progress

• forecast package?
• plotly package?

The TSstudio package provides a set of functions for time series analysis and forecasting such as:

• Utility functions for pre-processing time series data
• Interactive data visualization tools for descriptive analysis, based on the plotly package engine
• Set of functions for predictive analysis and forecasting automation with the use of models from the forecast, forecastHybrid, and bsts packages

The primary goal of the package is to simplify the analysis workflow (or, minimum code - maximum results)

Install from CRAN:

install.packages("TSstudio")

Or from Github:

devtools::install_github("RamiKrispin/TSstudio")

• Data visualization tools
• Seasonal analysis
• Correlation analysis
• Forecasting applications

Link to the demo

Forecast simulation

library(forecast)
library(TSstudio)
data(USgas)
md <- auto.arima(USgas)

fc_sim <- forecast_sim(md, h = 60, n = 200)

Forecast simulation

fc_sim$plot

HoltWinters tuning parameters with grid search

hw_grid_shallow <- ts_grid(ts.obj = USgas,
                            periods = 6,
                            model = "HoltWinters",
                            optim = "MAPE",
                            window_space = 6,
                            window_test = 12,
                            hyper_params = list(alpha = seq(0.01, 1,0.1),
                                                beta =  seq(0.01, 1,0.1),
                                                gamma = seq(0.01, 1,0.1)),
                            parallel = TRUE,
                            n.cores = 8)

HoltWinters tuning parameters with grid search

plot_grid(hw_grid_shallow, type = "parcoords")

HoltWinters tuning parameters with grid search

plot_grid(hw_grid_shallow, type = "3D", top = 200)

Backtesting with both expending and sliding windows and parallel option

fc <- ts_test(ts.obj = USgas,
             window_type = "expending",
             h = 60,
             window_space = 6,
             window_test = 12,
             parallel = TRUE,
             n_cores = 8,
             top = 6)

Backtesting with both expending and sliding windows and parallel option

plot_backtesting(fc,by = "MAPE", type = "all", palette = "viridis", top = NULL)