TSstudio

The TSstudio package provides a set of tools descriptive and predictive analysis of time series data. That includes utility functions for preprocessing time series data, interactive visualization functions based on the plotly package engine, and set of tools for training and evaluating time series forecasting models from the forecast, forecastHybrid, and bsts packages.

library(TSstudio)
data(USgas)
ts_seasonal(USgas, type = "all")

More information available on the package vignettes.

forecastLM

The forecastLM R package provides a framework for forecasting regular time-series data with linear regression models (based on the lm function). It supports both tsibble and ts objects as an input. More details available here.

coronavirus

The coronavirus package provides a tidy format dataset of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic. The raw data pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus repository.

UKgrid

The UKgrid dataset is an example of a multiple seasonality time series. This time series captures the demand for electricity and its components in the UK since April 2005 using half-hour intervals. In addition, the package provides a function to extract, subset and aggregate the series into tsibble, ts, xts, zoo, data.frame, data.table, or tbl.

The data was sourced from the National Grid UK website

library(TSstudio)
library(UKgrid)
nd_hourly <- UKgrid::extract_grid(type = "tsibble", 
                          columns = "ND",
                          aggregate = "Hourly")


ts_plot(ts.obj = nd_hourly, 
        title = "UK National Demand - Hourly")

USgrid

The USgrid R package provides a set of high frequency (hourly) time-series datasets, describing the demand and generation of electricity in the US (lower-48 states, excluding Alaska and Hawaii). That includes the following series:

• US_elec - the total hourly demand and supply (generation) for electricity in the US since July 2015

• US_source - the US net generation of electricity by energy source (natural gas, coal, solar, etc.) since July 2018

• Cal_elec - The California subregion hourly demand by operator since July 2018

All datasets are in tsibble format

library(USgrid)
library(plotly)

plot_ly(data = US_source,
        x = ~ date_time,
        y = ~ series,
        color = ~ source,
        type = "scatter",
        mode = "lines") %>%
  layout(title = "US Electricity Generation by Energy Source",
         yaxis = list(title = "Mwh"),
         xaxis = list(title = "Source: US Energy Information Administration (Nov 2019)"))