Time Series Clustering and Classification

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This page shows R code examples on time series clustering and classification with R.

Time Series Clustering

Time series clustering is to partition time series data into groups based on similarity or distance, so that time series in the same cluster are similar. For time series clustering with R, the first step is to work out an appropriate distance/similarity metric, and then, at the second step, use existing clustering techniques, such as k-means, hierarchical clustering, density-based clustering or subspace clustering, to find clustering structures.

Dynamic Time Warping (DTW) finds optimal alignment between two time series, and DTW distance is used as a distance metric in the example below.

A data set of Synthetic Control Chart Time Series is used here, which contains 600 examples of control charts. Each control chart is a time series with 60 values. There are six classes: 1) 1-100 Normal, 2) 101-200 Cyclic, 3) 201-300 Increasing trend, 4)301-400 Decreasing trend, 5) 401-500 Upward shift, and 6) 501-600 Downward shift. The dataset is downloadable at UCI KDD Archive.

> sc <- read.table(“E:/Rtmp/synthetic_control.data”, header=F, sep=”")

# randomly sampled n cases from each class, to make it easy for plotting

> n <- 10

> s <- sample(1:100, n)

> idx <- c(s, 100+s, 200+s, 300+s, 400+s, 500+s)

> sample2 <- sc[idx,]

> observedLabels <- c(rep(1,n), rep(2,n), rep(3,n), rep(4,n), rep(5,n), rep(6,n))

# compute DTW distances

> library(dtw)

> distMatrix <- dist(sample2, method=”DTW”)

# hierarchical clustering

> hc <- hclust(distMatrix, method=”average”)

> plot(hc, labels=observedLabels, main=”")

Time Series Classification

Time series classification is to build a classification model based on labelled time series and then use the model to predict the label of unlabelled time series. The way for time series classification with R is to extract and build features from time series data first, and then apply existing classification techniques, such as SVM, k-NN, neural networks, regression and decision trees, to the feature set.

Discrete Wavelet Transform (DWT) provides a multi-resolution representation using wavelets and is used in the example below. Another popular feature extraction technique is Discrete Fourier Transform (DFT).

# extracting DWT coefficients (with Haar filter)

> library(wavelets)

> wtData <- NULL

> for (i in 1:nrow(sc)) {

+ a <- t(sc[i,])

+ wt <- dwt(a, filter=”haar”, boundary=”periodic”)

+ wtData <- rbind(wtData, unlist(c(wt@W,wt@V[[wt@level]])))

+ }

> wtData <- as.data.frame(wtData)

# set class labels into categorical values

> classId <- c(rep(“1″,100), rep(“2″,100), rep(“3″,100),

+ rep(“4″,100), rep(“5″,100), rep(“6″,100))

> wtSc <- data.frame(cbind(classId, wtData))

# build a decision tree with ctree() in package party

> library(party)

> ct <- ctree(classId ~ ., data=wtSc,

+ controls = ctree_control(minsplit=30, minbucket=10, maxdepth=5))

> pClassId <- predict(ct)

# check predicted classes against original class labels

> table(classId, pClassId)

# accuracy

> (sum(classId==pClassId)) / nrow(wtSc)

[1] 0.8716667

> plot(ct, ip_args=list(pval=FALSE), ep_args=list(digits=0))

More examples on time series analysis and mining with R and other data mining techniques can be found in my book "R and Data Mining: Examples and Case Studies", which is downloadable as a .PDF file at the link.