## 1 Introduction
By noting the change in gradients between the baseline and the standard input and scaling against the input, it allows the identifications of informative features and, in doing so, removes the noise generated by the ablation method [18]. In contrast to these two methods, global explainability methods seek to explain which factors are essential for all predictions of a given system. The permutation method is such a method where the relative importance is determined based on the degree of change experienced in all predictions after single feature values are randomised [19]. Researchers have made less progress in explainable financial time series forecasting despite the diversity and improvement of explainability methods [20]. The slow progress in XAI research in time series forecasting stems from the previous lack of necessity, as inherently explainable statistical methods see preferential use over machine learning methods [21,22]. The delayed uptake of XAI methods in financial forecasting is apparent when considering the limited recently published methods pertaining to regression rather than classification problems [23,24]. Furthermore, existing time series XAI methods may not apply to financial forecasting; here, the aim of XAI is often to determine if models, prone to backtest overfitting, are fitting _patterns_ that are random noise [25]. Among the machine learning techniques for forecasting, practitioners often employ RNN architectures [26,27]. This preference is because they perform reasonably for sequential data, despite in many instances being overshadowed by exponential smoothing and autoregressive methods [28,29]. However, recent results suggest that an ensemble of purely deep learning methods provide a more accurate means of prediction, which has led to an increased usage of neural-network-based forecasting strategies [30,31]. As it stands, financial time series forecasting lags other fields in explainability in the number of available methods and use in industry, making application of these state-of-the-art methods difficult in conforming to current XAI governance policies [20]. The lack of progress in explainable forecasting raises the question of whether existing XAI methodologies are transferable to the financial time series forecasting context or if novel methods are needed. In addressing this question, the research presented here aims to establish a baseline for XAI in time series forecasting using existing methods in the hope of guiding future efforts into more accurate and context-specific XAI methods. In doing so, this study will support the uptake of increasingly accurate \"black box\" machine-learning forecasting methods by providing additional means to comply with XAI policies. This paper explores the feasibility of using established explainability methods, namely ablation, permutation, random noise, and integrated gradients, within the context of financial prediction and RNN architectures. Specifically, the baseline RNN, LSTM, and a GRU form the focus of this study. The results presented here demonstrate that existing XAI techniques are transferable to financial time series forecasting RNN architectures, providing insight into how each model makes predictions. The presented analysis provides evidence for the applicability among existing XAI methods in financial time series forecasting, providing robust complementary results. Furthermore, we have outlined an alteration to the random noise method more applicable to recurrent neural network architectures. Collectively, these results form a baseline for future research aimed at developing methods specific to explainable time series forecasting machine learning strategies.