timetk: A toolkit for time series analysis in the tidyverse

This tutorial focuses on, `plot_time_series()`

, a workhorse time-series plotting function that:

- Generates interactive
`plotly`

plots (great for exploring & shiny apps) - Consolidates 20+ lines of
`ggplot2`

&`plotly`

code - Scales well to many time series
- Can be converted from interactive
`plotly`

to static`ggplot2`

plots

```
library(tidyverse)
library(lubridate)
library(timetk)
# Setup for the plotly charts (# FALSE returns ggplots)
interactive <- FALSE
```

Let’s start with a popular time series, `taylor_30_min`

, which includes energy demand in megawatts at a sampling interval of 30-minutes. This is a single time series.

```
taylor_30_min
#> # A tibble: 4,032 x 2
#> date value
#> <dttm> <dbl>
#> 1 2000-06-05 00:00:00 22262
#> 2 2000-06-05 00:30:00 21756
#> 3 2000-06-05 01:00:00 22247
#> 4 2000-06-05 01:30:00 22759
#> 5 2000-06-05 02:00:00 22549
#> 6 2000-06-05 02:30:00 22313
#> 7 2000-06-05 03:00:00 22128
#> 8 2000-06-05 03:30:00 21860
#> 9 2000-06-05 04:00:00 21751
#> 10 2000-06-05 04:30:00 21336
#> # ... with 4,022 more rows
```

The `plot_time_series()`

function generates an interactive `plotly`

chart by default.

- Simply provide the date variable (time-based column,
`.date_var`

) and the numeric variable (`.value`

) that changes over time as the first 2 arguments - When
`.interactive = TRUE`

, the`.plotly_slider = TRUE`

adds a date slider to the bottom of the chart.

Next, let’s move on to a dataset with time series groups, `m4_daily`

, which is a sample of 4 time series from the M4 competition that are sampled at a daily frequency.

```
m4_daily %>% group_by(id)
#> # A tibble: 9,743 x 3
#> # Groups: id [4]
#> id date value
#> <fct> <date> <dbl>
#> 1 D10 2014-07-03 2076.
#> 2 D10 2014-07-04 2073.
#> 3 D10 2014-07-05 2049.
#> 4 D10 2014-07-06 2049.
#> 5 D10 2014-07-07 2006.
#> 6 D10 2014-07-08 2018.
#> 7 D10 2014-07-09 2019.
#> 8 D10 2014-07-10 2007.
#> 9 D10 2014-07-11 2010
#> 10 D10 2014-07-12 2002.
#> # ... with 9,733 more rows
```

Visualizing grouped data is as simple as grouping the data set with `group_by()`

prior to piping into the `plot_time_series()`

function. Key points:

- Groups can be added in 2 ways: by
`group_by()`

or by using the`...`

to add groups. - Groups are then converted to facets.
`.facet_ncol = 2`

returns a 2-column faceted plot`.facet_scales = "free"`

allows the x and y-axis of each plot to scale independently of the other plots

Let’s switch to an hourly dataset with multiple groups. We can showcase:

- Log transformation to the
`.value`

- Use of
`.color_var`

to highlight sub-groups.

```
m4_hourly %>% group_by(id)
#> # A tibble: 3,060 x 3
#> # Groups: id [4]
#> id date value
#> <fct> <dttm> <dbl>
#> 1 H10 2015-07-01 12:00:00 513
#> 2 H10 2015-07-01 13:00:00 512
#> 3 H10 2015-07-01 14:00:00 506
#> 4 H10 2015-07-01 15:00:00 500
#> 5 H10 2015-07-01 16:00:00 490
#> 6 H10 2015-07-01 17:00:00 484
#> 7 H10 2015-07-01 18:00:00 467
#> 8 H10 2015-07-01 19:00:00 446
#> 9 H10 2015-07-01 20:00:00 434
#> 10 H10 2015-07-01 21:00:00 422
#> # ... with 3,050 more rows
```

The intent is to showcase the groups in faceted plots, but to highlight weekly windows (sub-groups) within the data while simultaneously doing a `log()`

transformation to the value. This is simple to do:

`.value = log(value)`

Applies the Log Transformation`.color_var = week(date)`

The date column is transformed to a`lubridate::week()`

number. The color is applied to each of the week numbers.

All of the visualizations can be converted from interactive `plotly`

(great for exploring and shiny apps) to static `ggplot2`

visualizations (great for reports).

```
taylor_30_min %>%
plot_time_series(date, value,
.color_var = month(date, label = TRUE),
# Returns static ggplot
.interactive = FALSE,
# Customization
.title = "Taylor's MegaWatt Data",
.x_lab = "Date (30-min intervals)",
.y_lab = "Energy Demand (MW)",
.color_lab = "Month") +
scale_y_continuous(labels = scales::comma_format())
```

*My Talk on High-Performance Time Series Forecasting*

Time series is changing. **Businesses now need 10,000+ time series forecasts every day.**

**High-Performance Forecasting Systems will save companies MILLIONS of dollars.** Imagine what will happen to your career if you can provide your organization a “High-Performance Time Series Forecasting System” (HPTSF System).

I teach how to build a HPTFS System in my **High-Performance Time Series Forecasting Course**. If interested in learning Scalable High-Performance Forecasting Strategies then take my course. You will learn:

- Time Series Machine Learning (cutting-edge) with
`Modeltime`

- 30+ Models (Prophet, ARIMA, XGBoost, Random Forest, & many more) - NEW - Deep Learning with
`GluonTS`

(Competition Winners) - Time Series Preprocessing, Noise Reduction, & Anomaly Detection
- Feature engineering using lagged variables & external regressors
- Hyperparameter Tuning
- Time series cross-validation
- Ensembling Multiple Machine Learning & Univariate Modeling Techniques (Competition Winner)
- Scalable Forecasting - Forecast 1000+ time series in parallel
- and more.