This article provides a comprehensive survey of deep learning methods for time series forecasting, highlighting key architectures, techniques, and applications. It discusses various deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and attention mechanisms, and their applications in time series forecasting. The paper also explores multi-horizon forecasting models, which predict multiple future time points, and discusses the use of hybrid models that combine statistical and deep learning approaches to improve forecasting accuracy. Additionally, the article addresses the importance of interpretability and counterfactual predictions in decision support systems, emphasizing the need for models that can explain their predictions and provide insights into potential future scenarios. The survey also highlights the challenges and limitations of deep learning in time series forecasting, such as the need for regular time intervals and the complexity of hierarchical structures in time series data. Overall, the paper provides a detailed overview of the current state of deep learning in time series forecasting, emphasizing the potential of these methods in various applications.This article provides a comprehensive survey of deep learning methods for time series forecasting, highlighting key architectures, techniques, and applications. It discusses various deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and attention mechanisms, and their applications in time series forecasting. The paper also explores multi-horizon forecasting models, which predict multiple future time points, and discusses the use of hybrid models that combine statistical and deep learning approaches to improve forecasting accuracy. Additionally, the article addresses the importance of interpretability and counterfactual predictions in decision support systems, emphasizing the need for models that can explain their predictions and provide insights into potential future scenarios. The survey also highlights the challenges and limitations of deep learning in time series forecasting, such as the need for regular time intervals and the complexity of hierarchical structures in time series data. Overall, the paper provides a detailed overview of the current state of deep learning in time series forecasting, emphasizing the potential of these methods in various applications.