Time Series clustering and classification

Http://www.rdatamining.com/examples/time-series-clustering-classification

Time Series clustering and classification This page shows r code examples in 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 I n the same cluster is similar. For time series clustering with R, the first step was to work out a appropriate distance/similarity metric, and then, at t He 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 the 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-examples of control charts. Each control chart was a time series with the values. There is 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 pred ICT the label of Unlabelled time series. The On-time series classification with 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 Belo W. 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 () > 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 is found in my book "R and D ATA Mining:examples and Case Studies ", which is downloadable as a. PDF file at the link.