Time Series Clustering
Matt Dancho
2024-01-04
Source:vignettes/TK09_Clustering.Rmd
TK09_Clustering.RmdClustering 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.
Data
This tutorial will use the walmart_sales_weekly
dataset:
- Weekly
- Sales spikes at various events
walmart_sales_weekly## # A tibble: 1,001 × 17
## id Store Dept Date Weekly_Sales IsHoliday Type Size Temperature
## <fct> <dbl> <dbl> <date> <dbl> <lgl> <chr> <dbl> <dbl>
## 1 1_1 1 1 2010-02-05 24924. FALSE A 151315 42.3
## 2 1_1 1 1 2010-02-12 46039. TRUE A 151315 38.5
## 3 1_1 1 1 2010-02-19 41596. FALSE A 151315 39.9
## 4 1_1 1 1 2010-02-26 19404. FALSE A 151315 46.6
## 5 1_1 1 1 2010-03-05 21828. FALSE A 151315 46.5
## 6 1_1 1 1 2010-03-12 21043. FALSE A 151315 57.8
## 7 1_1 1 1 2010-03-19 22137. FALSE A 151315 54.6
## 8 1_1 1 1 2010-03-26 26229. FALSE A 151315 51.4
## 9 1_1 1 1 2010-04-02 57258. FALSE A 151315 62.3
## 10 1_1 1 1 2010-04-09 42961. FALSE A 151315 65.9
## # ℹ 991 more rows
## # ℹ 8 more variables: Fuel_Price <dbl>, MarkDown1 <dbl>, MarkDown2 <dbl>,
## # MarkDown3 <dbl>, MarkDown4 <dbl>, MarkDown5 <dbl>, CPI <dbl>,
## # Unemployment <dbl>TS Features
Using the tk_tsfeatures() function, we can quickly get
the “tsfeatures” for each of the time series. A few important
points:
The
featuresparameter come from thetsfeaturesR package. Use one of the function names fromtsfeaturesR 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## # A tibble: 7 × 22
## id ts_frequency ts_nperiods ts_seasonal_period ts_trend ts_spike
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1_1 52 1 52 0.000670 0.0000280
## 2 1_3 52 1 52 0.0614 0.00000987
## 3 1_8 52 1 52 0.756 0.00000195
## 4 1_13 52 1 52 0.354 0.00000475
## 5 1_38 52 1 52 0.425 0.0000179
## 6 1_93 52 1 52 0.791 0.000000754
## 7 1_95 52 1 52 0.639 0.000000567
## # ℹ 16 more variables: ts_linearity <dbl>, ts_curvature <dbl>, ts_e_acf1 <dbl>,
## # ts_e_acf10 <dbl>, ts_seasonal_strength <dbl>, ts_peak <dbl>,
## # ts_trough <dbl>, ts_entropy <dbl>, ts_x_acf1 <dbl>, ts_x_acf10 <dbl>,
## # ts_diff1_acf1 <dbl>, ts_diff1_acf10 <dbl>, ts_diff2_acf1 <dbl>,
## # ts_diff2_acf10 <dbl>, ts_seas_acf1 <dbl>, ts_my_mean <dbl>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## # A tibble: 7 × 23
## cluster id ts_frequency ts_nperiods ts_seasonal_period ts_trend ts_spike
## <int> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2 1_1 52 1 52 0.000670 0.0000280
## 2 2 1_3 52 1 52 0.0614 0.00000987
## 3 2 1_8 52 1 52 0.756 0.00000195
## 4 1 1_13 52 1 52 0.354 0.00000475
## 5 3 1_38 52 1 52 0.425 0.0000179
## 6 3 1_93 52 1 52 0.791 0.000000754
## 7 1 1_95 52 1 52 0.639 0.000000567
## # ℹ 16 more variables: ts_linearity <dbl>, ts_curvature <dbl>, ts_e_acf1 <dbl>,
## # ts_e_acf10 <dbl>, ts_seasonal_strength <dbl>, ts_peak <dbl>,
## # ts_trough <dbl>, ts_entropy <dbl>, ts_x_acf1 <dbl>, ts_x_acf10 <dbl>,
## # ts_diff1_acf1 <dbl>, ts_diff1_acf10 <dbl>, ts_diff2_acf1 <dbl>,
## # ts_diff2_acf10 <dbl>, ts_seas_acf1 <dbl>, ts_my_mean <dbl>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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