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Introduction

In recent years, deep learning has become a popular and powerful approach for solving complex machine learning problems, including time series forecasting. However, one of the main challenges with deep learning models is their lack of interpretability, making it difficult for users to understand how the model arrives at its predictions. This has led to a growing interest in the field of explainable artificial intelligence (XAI), which aims to provide transparency and interpretability to deep learning models.

This thesis focuses on the application of explainable deep learning techniques for time series forecasting. By incorporating explainability into deep learning models, we aim to improve the transparency and interpretability of the forecasting process, allowing users to better understand and trust the predictions made by the model.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Overview of Deep Learning
2.2 Time Series Forecasting
2.3 Explainable Artificial Intelligence
2.4 Interpretable Machine Learning
2.5 Deep Learning Models for Time Series Forecasting
2.6 XAI Techniques for Time Series Forecasting
2.7 Applications of XAI in Industry
2.8 Challenges in Forecasting with Deep Learning
2.9 Advantages of Explainable Deep Learning
2.10 Current Research in the Field

Chapter 3: Research Methodology
3.1 Data Collection
3.2 Data Preprocessing
3.3 Model Architecture
3.4 Explainable Deep Learning Techniques
3.5 Evaluation Metrics
3.6 Experimental Setup
3.7 Training and Testing
3.8 Analysis Framework

Chapter 4: Discussion of Findings
4.1 Model Performance
4.2 Interpretability of Predictions
4.3 Comparison with Traditional Methods
4.4 Case Studies
4.5 Impact of Explainability on Decision Making
4.6 Limitations and Challenges
4.7 Future Research Directions

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Practical Implications
5.4 Recommendations for Future Research
5.5 Conclusion

Thesis Overview:

The field of time series forecasting has seen significant advancements with the rise of deep learning techniques. However, the lack of transparency and interpretability in deep learning models has posed challenges for users in understanding and trusting the predictions made by these models. This has led to the emergence of explainable artificial intelligence (XAI) as a key area of research, aiming to provide transparency and explainability to complex machine learning models.

This thesis focuses on the application of explainable deep learning techniques for time series forecasting, with the aim of improving the interpretability of deep learning models in this domain. The study will begin with a comprehensive literature review on deep learning, time series forecasting, and XAI, followed by a detailed overview of the research methodology employed for the study. The findings from the research will be discussed in depth, including an analysis of model performance, interpretability of predictions, and comparison with traditional forecasting methods. The thesis will conclude with a summary of findings, contributions to the field, and recommendations for future research.

Overall, this thesis aims to contribute to the growing body of research on explainable deep learning for time series forecasting, providing valuable insights into the application of XAI techniques in improving the transparency and interpretability of deep learning models in this domain.

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