In business-science/timetk: A Tool Kit for Working with Time Series

knitr::opts_chunk$set(
    message = FALSE,
    warning = FALSE,
    fig.width = 8, 
    fig.height = 4.5,
    fig.align = 'center',
    out.width='95%', 
    dpi = 100
)

# devtools::load_all() # Travis CI fails on load_all()

Clustering is an important part of time series analysis that allows us to organize time series into groups by combining "tsfeatures" (summary matricies) with unsupervised techniques such as K-Means Clustering. In this short tutorial, we will cover the tk_tsfeatures() functions that computes a time series feature matrix of summarized information on one or more time series.

Libraries

To get started, load the following libraries.

library(dplyr)
library(purrr)
library(timetk)

Data

This tutorial will use the walmart_sales_weekly dataset:

Weekly
Sales spikes at various events

walmart_sales_weekly

TS Features

Using the tk_tsfeatures() function, we can quickly get the "tsfeatures" for each of the time series. A few important points:

The features parameter come from the tsfeatures R package. Use one of the function names from tsfeatures R package e.g.("lumpiness", "stl_features").
We can supply any function that returns an aggregation (e.g. "mean" will apply the base::mean() function).
You can supply custom functions by creating a function and providing it (e.g. my_mean() defined below)

# Custom Function
my_mean <- function(x, na.rm=TRUE) {
  mean(x, na.rm = na.rm)
}

tsfeature_tbl <- walmart_sales_weekly %>%
    group_by(id) %>%
    tk_tsfeatures(
      .date_var = Date,
      .value    = Weekly_Sales,
      .period   = 52,
      .features = c("frequency", "stl_features", "entropy", "acf_features", "my_mean"),
      .scale    = TRUE,
      .prefix   = "ts_"
    ) %>%
    ungroup()

tsfeature_tbl

Clustering with K-Means

We can quickly add cluster assignments with the kmeans() function and some tidyverse data wrangling.

set.seed(123)


cluster_tbl <- tibble(
    cluster = tsfeature_tbl %>% 
        select(-id) %>%
        as.matrix() %>%
        kmeans(centers = 3, nstart = 100) %>%
        pluck("cluster")
) %>%
    bind_cols(
        tsfeature_tbl
    )

cluster_tbl

Visualize the Cluster Assignments

Finally, we can visualize the cluster assignments by joining the cluster_tbl with the original walmart_sales_weekly and then plotting with plot_time_series().

cluster_tbl %>%
    select(cluster, id) %>%
    right_join(walmart_sales_weekly, by = "id") %>%
    group_by(id) %>%
    plot_time_series(
      Date, Weekly_Sales, 
      .color_var   = cluster, 
      .facet_ncol  = 2, 
      .interactive = FALSE
    )

Learning More

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