**4. Conclusions and future work recommendations**

This work demonstrated the effectiveness of a multivariate analysis using six different unsupervised ML algorithms for time series. To verify the performance of the unsupervised ML algorithms to detect interesting/anomalous patterns in real time series data, the six algorithms were applied in two different real cases: (i) meteocean data in hurricane season and (ii) monitoring data from dynamic industrial machinery. The experimental results showed that clustering methods as C-AMDATS have higher capacity to recognize and isolate the anomaly region, revealing the ability to assist experts to label raw data with unsupervised ML algorithms with great performance.

Future works include the extension of this analysis to more real cases aiming to develop a broader analysis, as well as an extensive study and investigation of approaches of semi-supervised learning to train deep learning algorithms to predict and classify unknown data in different dataset.
