# Similarity Measurement and Classification of Temporal Data Based on Double Mean Representation
## 4 Experimental Validation
### Experimental Results and Analysis
However, when the time series data have a small number of segments, the number of symbols required for the approximation representation increases. In contrast, the SAX-DM method proposed in this paper can represent any segment of the time series data with two symbols, while still achieving a comparable classification accuracy to SAX-TD. Therefore, the SAX-DM method is more suitable for subsequent similarity query tasks on massive time series data. Additionally, the mean and trend distance can be further encoded to enhance compression and dimensionality reduction, thereby improving the compression ratio of the time series data approximation method. The comparison results between the SAX-DM and SAX-BD methods are shown in Figure 8. From the figure, it can be observed that the SAX-BD method performed better in the classification task on 60% of the datasets, and its classification accuracy varied significantly across different datasets. Considering the reasons behind this, the SAX-BD method incorporates the left and right extreme values in addition to the mean value to represent the time series data, while the SAX-DM method focuses on extracting the mean feature. Therefore, there is a significant difference in the expression effect of these two methods for data with smooth or drastic changes. Although SAX-BD achieves higher classification accuracy, it uses three features for dimensionality reduction, resulting in a lower compression ratio, which makes it unsuitable for subsequent similarity query tasks on large-scale time series data. Figure 9 illustrates the comparison of classification error rates among the SAX, SAX-TD, SAX-BD, and SAX-DM methods using the classic ECG dataset as experimental data. Different \(\alpha\) and \(\mathrm{w}\) parameters were set for the evaluation. From the graph, it can be observed that the \(\alpha\) parameter had a negligible impact on the classification accuracy, while the \(\mathrm{w}\) parameter, when too large or too small, affected the accuracy. Based on the results depicted in the graph, the optimal value for the \(\mathrm{w}\) parameter can be chosen as 16. Since comparing compression ratios alone cannot fully demonstrate the advantages of expression methods, Figure 10 compares the classification accuracy of SAX, SAX-TD, SAX-BD, and SAX-DM at the same compression ratio, using SAX's compression ratio as the benchmark. A higher value in the graph indicates better overall performance in terms of compression ratio and accuracy. It is evident from the graph that SAX-DM method performed exceptionally well and had a more convenient index item conversion method. In general, the SAX-DM method is more suitable for similarity queries in time series data, especially in scenarios that require a large number of iterations, as it effectively reduces computer memory pressure and enhances data mining efficiency. Figure 7: Comparison results of SAX-DM and SAX-TD. Figure 8: Comparison results of SAX-DM and SAX-BD. Figure 9: Comparison of classification error rates under different \(\alpha\) and w conditions.