---
title: "Time Series Clustering"
author: "Matt Dancho"
date: "`r Sys.Date()`"
output:
rmarkdown::html_vignette:
toc: true
toc_depth: 2
vignette: >
%\VignetteIndexEntry{Time Series Clustering}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, echo = FALSE, message = FALSE, warning = FALSE}
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.
```{r}
library(dplyr)
library(purrr)
library(timetk)
```
# Data
This tutorial will use the `walmart_sales_weekly` dataset:
- Weekly
- Sales spikes at various events
```{r}
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)
```{r, fig.height=7}
# 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.
```{r}
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()`.
```{r}
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
_My Talk on High-Performance Time Series Forecasting_
Time series is changing. __Businesses now need 10,000+ time series forecasts every day.__ This is what I call a _High-Performance Time Series Forecasting System (HPTSF)_ - Accurate, Robust, and Scalable Forecasting.
__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](https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting). 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.