Table 10: An evaluation (heuristic) function.

Function [index] = **Evaluate**[D,B]

![0_image_1.png](0_image_1.png)

$$b(i,Band)=\min_{c\in C\wedge c\neq label(i)}\left(\frac{1}{N_{D_{c}}}\sum_{j\in D_{e}}d(i,j,Band_{label(j)})\right)\tag{9}$$
a(**i, Band**) = 1
$$ad)=\frac{1}{N_{D_{c}}}\sum_{j\in D_{label(j)}\wedge i\neq j}d(i,j,Band_{label(j)})\tag{10}$$

## 3.6 Data Prediction

After the best multi R-K bands are discovered, we use 1-
Nearest Neighbor as a classifier and the Dynamic Time Warping distance measure with these best R-K bands for prediction in the test data to predict a set of unlabeled data. The LB Keogh lower bound is also used to speed up the DTW
computation.

## 4. Experimental Evaluation

To evaluate the performance, we use our approach, described in Section 3, to classify all 20 contest datasets, and then send our predicted results and the expected accuracies to the contest organizer. The results are calculated by the contest organizer and subsequently sent back to us.

## 4.1 Datasets

We use the datasets from the Workshop and Challenge on Time Series Classification, held in conjunction with the thirteenth SIGKDD 2007 conference. The datasets are from very diverse domains (e.g., stock data, medical data, etc.);
some are from real-world problems, and some are synthetically generated. The amount of training data and its length in each dataset also vary from the size of 20 to 1000 training instances and the length of 30 to 2000 data points.

In addition, all data are individually normalized using Znormalization. Examples of each dataset are shown in Figure 5, and the datasets' properties are shown in Table 11.

![0_image_0.png](0_image_0.png)

