This paper provides a comprehensive survey of deep learning-based time series forecasting (TSF) models, summarizing recent developments and offering a detailed taxonomy of models based on neural network architectures. The authors analyze the evolution of TSF models over the past five years, categorizing them into four main types: RNN-based, CNN-based, Transformer-based, and MLP-based. They also discuss common experimental evaluation metrics and datasets used in TSF research, identify current challenges in deep learning-based TSF, and suggest promising future directions for research in this field. The paper highlights the importance of TSF in various domains, including economics, meteorology, transportation, and healthcare, and emphasizes the role of deep learning in capturing complex real-world phenomena. The authors note that while traditional statistical models have been widely used for TSF, deep learning models have shown superior performance in capturing long-term dependencies and complex patterns in time series data. However, deep learning models still face challenges such as handling large-scale data, achieving longer forecasting ranges, and reducing computational complexity. The paper also discusses the limitations of existing TSF models and proposes potential innovations and research directions in the field. The authors conclude that deep learning-based TSF models are an important area of AI research and provide a detailed overview of the current state of the field, serving as a valuable resource for researchers and practitioners in this area.This paper provides a comprehensive survey of deep learning-based time series forecasting (TSF) models, summarizing recent developments and offering a detailed taxonomy of models based on neural network architectures. The authors analyze the evolution of TSF models over the past five years, categorizing them into four main types: RNN-based, CNN-based, Transformer-based, and MLP-based. They also discuss common experimental evaluation metrics and datasets used in TSF research, identify current challenges in deep learning-based TSF, and suggest promising future directions for research in this field. The paper highlights the importance of TSF in various domains, including economics, meteorology, transportation, and healthcare, and emphasizes the role of deep learning in capturing complex real-world phenomena. The authors note that while traditional statistical models have been widely used for TSF, deep learning models have shown superior performance in capturing long-term dependencies and complex patterns in time series data. However, deep learning models still face challenges such as handling large-scale data, achieving longer forecasting ranges, and reducing computational complexity. The paper also discusses the limitations of existing TSF models and proposes potential innovations and research directions in the field. The authors conclude that deep learning-based TSF models are an important area of AI research and provide a detailed overview of the current state of the field, serving as a valuable resource for researchers and practitioners in this area.