## 1 Introduction  
Artificial intelligence (AI) is increasingly becoming an integral part of society, and is applied in fields ranging from finance to healthcare and computer vision [1, 2, 3]. However, there is an inverse relationship between explainability in a system and its predictive accuracy [4]. Consequently, neural networks and deep learning, which offer the highest accuracy for tasks such as natural language processing and computer vision, are also the least interpretable. Equally, their inherent complexity and nonlinear nature enable such models to learn abstract patterns whilst leading to difficulties in explaining their predictions. As a result, researchers have classed such methods as being a \"black box\", which runs the risks of perpetuating computer-based discrimination and bias [5]. Furthermore, in the worst-case scenario, failing to understand how an AI system may function could lead to physical harm as AI becomes further integrated into society [6]. Recognising the necessity of explainable AI (XAI) has seen a concerted effort by governments to implement laws concerning the implementation of explainable prediction systems [7]. Notably, XAI has seen significant use and advancement in computer vision and natural language processing, since it allows one to contrast how machines and humans learn. Opaque models, such as neural networks, are those requiring post hoc analyses in order to achieve explainability. Such post hoc analyses can further be divided into model-agnostic and model-specific [8,9]. Currently, the Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) values are foremost choices when it comes to agnostic XAI methods [10,11]. Practitioners commonly use these methods to generate counterfactual explanations that describe the necessary changes required in an input to change a classification [12]. While LIME and SHAP are desirable for their model-agnostic property, researchers have also observed success among model-specific XAI methods. Recent advances in time series XAI methods focus on convolutional neural networks (CNN) applications. The XCM algorithm developed by [PERSON] et al. provides insights behind feature importance through time and feature attribution maps in various health data [13]. This algorithm boasts granularity in both global and local explainability and is based on gradient-weighted class activation mapping (Grad-CAM) [14]. Additionally, [PERSON] et al. similarly incorporated the use of a CNN to generate heatmaps to describe feature and time importance surrounding the decline of patients in health datasets [15]. Both methods demonstrate the practicality of CNN in determining feature importance in deep classification networks. However, these methods prove ineffective for financial time series forecasting. The methods are either not applicable to time series data or are directed towards classification algorithms, further highlighting the need for XAI methods for financial forecasting models. In devising such an algorithm, insight can be gained from examining XAI methods used in other fields. Ablation is one of the first local explainability methods used in computer vision. Ablation infers image pixel importance by measuring the change in prediction when removing information from a region [16]. Similar to LIME, ablation is a local method that explains a specific prediction for a given input. However, ablation carries the disadvantage that the size and shape of the ablation pattern may lead to false positive and false negative in determining importance among features. Furthermore, the ablation pattern may fail to account for feature interactions [17]. The integrated gradients approach uses backpropagation of attributions of the image against a blank baseline to improve the ablation method. Similar to the blanked region in ablation, this baseline means to represent a state of no information.