## 5 Conclusions  
The overarching goal of this research was to determine if existing XAI methods were transferable to financial time series prediction models. All four presented methods provided complementary results despite testing different aspects of the network, providing a robust means in understanding how each of the three models came about their prediction. Collectively, these results provided insight behind where models place importance on input features and revealed contrasting strategies of prediction fine-tuning between architectures. These results suggest that the principles that form the basis of the explainability methods are applicable in financial time series forecasting and can provide an understanding of their complex architectures. An important limitation of this study is that the methods applied here were not tested on a comprehensive set of architectures, opting for the three standard RNN models instead. The performance of these methods may not apply to all deep learning strategies, as it relies on visualisation to derive meaning. A further limitation is that the methods may not be a suitable fit for CNNs, which are on occasion employed in the finance sector [44,45]. Furthermore, whilst the different error metrics provided an understanding for different aspects of the network, they may not prove intuitive to the average person seeking to benefit from explainable AI. Given these limitations, the recommendation is that future studies should expand to see if such methods apply in deep forecasting strategies and other popular architectures such as CNN. A further recommendation is the development of less abstract metrics to impart more practical information regarding model mechanisms.  
Conceptualization, T.v.Z.; methodology, W.F. and T.v.Z.; software, W.F.; validation, W.F.; formal analysis, W.F.; investigation, W.F.; resources, T.v.Z.; data curation, W.F.; writing--original draft preparation, W.F.; writing--review and editing, W.F. and T.v.Z.; visualization, W.F.; supervision, T.v.Z.; project administration, T.v.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the DSI-NICIS National e-Science Postgraduate Teaching and Training Platform.  
**Institutional Review Board Statement:** Not applicable  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Publicly available datasets were analysed in this study. These data and code to reproduce results can be found here: [[https://github.com/Warren-Freeborough/Explainable-RNN](https://github.com/Warren-Freeborough/Explainable-RNN)]([https://github.com/Warren-Freeborough/Explainable-RNN](https://github.com/Warren-Freeborough/Explainable-RNN)), accessed on 10 December 2021.  
**Acknowledgments:** The support of the DSI-NICIS National e-Science Postgraduate Teaching and Training Platform (NEPTTP) towards this research is hereby acknowledged. Opinions expressed and conclusions arrived at, are those of the authors and are not necessarily to be attributed to the NEPTTP.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.