# Scale-BOSS-MR: Scalable Time Series Classification Using Multiple Symbolic Representations +
## 5 Conclusions  
In this paper, we extend the SCALE-BOSS framework by incorporating into the process multiple time series representations using multiple time series' windows' sizes combined with multiple dilation parameters and applied to the original time series, as well as to the first-order differences' time series encoding trend information. Specifically, SCALE-BOSS-MR presents an improved version of SCALE-BOSS [9], incorporating multiple time series symbol-only representations, drawing inspiration from many diverse state-of-the-art TSC algorithms, with the objective to achieve state-of-the-art accuracy without increasing the computational cost of TSC methods. In so doing, SCALE-BOSS-MR incorporates (a) first-order differences to encode trend information similarly to Multi-Rocket [10], (b) different window sizes over the time series similarly to WEASEL [18], and (c) multiple dilation parameters similarly to Rocket [7] and WEASEL 2.0 [11]. The major findings are as follows: Adding trend information improved TSC accuracy, while maintaining scalability in terms of execution time. Using both multiple window sizes and trend information, the produced algorithms reach state-of-the-art accuracy. Specifically:  
1. Trend encoding helps both algorithms in the single window case and when using multiple window sizes;
2. Adding dilation configurations to the SCALE-BOSS-MR workflow helps significantly, but it increases the execution time considerably when combined with multiple window configurations;
3. Adding multiple window sizes to the SCALE-BOSS-MR workflow also helps in significantly increasing accuracy;
4. Using multiple window sizes, trend information, and dilation, we can achieve state-of-the-art accuracy. In addition, the resulting method can be tuned to be as efficient as other state-of-the-art methods exploiting symbolic time series representations;
5. Adding a few representations resulting from different dilation configurations improves accuracy while at the same time retaining scalability. This is validated since D6 is only marginally worse than D3 in terms of accuracy, but it is significantly faster and is also combined with suitable window configurations;
6. Ridge regression with cross validation performed the best in terms of accuracy compared to the Random Forest classifier;
7. SCALE-BOSS-MR is only 1% less accurate compared to state-of-the-art algorithms when tuned for accuracy, while it can be significantly faster than state-of-the-art algorithms with minimal loss in accuracy (2-3%) when tuned for scalability. As future work, we plan to port SCALE-BOSS-MR on a parallel processing framework such as Apache Spark [32], which is similar to [15]. This will allow for (a) incorporating additional time series representations to further increase accuracy in more efficient ways; (b) investigating using multiple instantiations of SCALE-BOSS-MR in an ensemble without compromising efficiency; and (c) providing experimental results with real-world datasets, with big data characteristics and application/domain-related characteristics (e.g., delay). \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Accuracy\_Mean** & **Accuracy\_Pop\_std** & **Total\_Time\_Mean** & **Total\_Time\_Pop\_std** \\
**Algorithm** & & & & \\ \hline WEASEL\_V2 & 0.904 & 0.078 & 339.6 & 189.9 \\ Rocket & 0.901 & 0.084 & 309.3 & 167.7 \\ MiniRocket & 0.900 & 0.082 & 18.7 & 20.1 \\ MRSQM\_nsax0\_nsfa5 & 0.889 & 0.093 & 418.8 & 239.4 \\ MRSQM\_nsax1\_nsfa1 & 0.878 & 0.101 & 176.2 & 101.1 \\ MRSQM\_nsax0\_nsfa1 & 0.861 & 0.109 & 117.2 & 76.1 \\ MRSQM\_nsax1\_nsfa0 & 0.839 & 0.113 & 104.2 & 59.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Accuracy and execution time of state-of-the-art algorithms. Furthermore, while SCALE-BOSS-MR shows that it can be tuned to accuracy vs. computational efficiency, we need effective ways of determining the appropriate configurations to address real-world requirements: This is an important part of future work as well.