## 4 Conclusions and Future Work  
This paper proposed a novel hybridisation of the LSTM and Capsule Networks in a multi-channel architecture to address the issues found in the literature review with the training performance of NNs, specifically on multivariate data. The motivation for this research stemmed from the growing demand for more effective unsupervised data analysis techniques regarding outlier and anomaly detection for use in industrial and commercial environments with large datasets to assist in the advancement of Industry 4.0--the automation of industrial processes. The proposed NN architecture was first tested on a drone dataset to observe the training and anomaly detection performance improvements with regards to non-hybridised and single channel variants of the NN, where it was found that, due to the inclusion of Capsules, the proposed NN can train more efficiently over a smaller number of epochs by converging at a faster rate in comparison to the variants with no Capsules integrated in the NN, and shows evidence of a resilience to overfitting. The tested NN models detected anomalies in the test data through an unsupervised method of reconstructing the validation data and using the maximum prediction MAE of the subset of data as a reference point for the confidence of prediction in any unseen data, so any data outlying from the expected shape in the training data would be flagged due to high prediction error. The results of this test concluded that the proposed NN architecture performs better than the other variants tested as a result of the proposed additions and changes to the NN architecture. The model variants were also tested on imperfect training data with outliers present, and all variants were found to be robust to outliers in the data due to the loss function used. Furthermore, due to the outliers present in the data not corresponding to faulty operation, the performance of the models on detecting faulty drone operation was not affected. It was also noted that training data that contains fault data would be easily spotted during the data collection or data analysis stages, and would therefore be easy to avoid when used to train the model. The proposed NN was also tested against other popular and state-of-the-art anomaly detection methods on the SKAB anomaly detection benchmark, where with slight hyperparameter adjustments the proposed method was able to adapt effectively to both outlier detection and changepoint detection, performing better than all other methods tested for outlier detection. Whilst the proposed method performed better than most methods in changepoint detection, the model was outperformed by the Isolation Forest algorithm. However, the Isolation Forest performed poorly with outlier detection, making it highly unsuitable as a hybrid solution for outlier and changepoint detection simultaneously. On the other hand, the proposed model, when optimised for changepoint detection, outperformed all other methods as a hybrid solution to both outlier and changepoint detection problems. Whilst the proposed NN operated exclusively on raw data, it was found in the literature review that with different representations of data, the prominence of data features can be increased, which in turn can help to improve the performance of unsupervised anomaly detection. However, feature representations would be based on the problem domain that the proposed model is being applied to and data collection specifications such as sampling frequency. Furthermore, the method of single datapoint outlier detection was found to have drawbacks to practical application due to single datapoint outliers being weighted equally to clustered datapoint outliers, which results in high False Positive rates for the model.