A Survey of Time Series Data Forecasting Methods Based on Deep Learning

Time Series Forecasting (TSF) involves predicting future values and trends of data at specific points or periods by analyzing historical patterns, such as trends and seasonality. With the advent of IoT sensors, traditional machine learning approaches struggle to handle massive time series datasets. Recently, deep learning algorithms, exemplified by convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Transformer models, have made significant progress in time series forecasting tasks. This paper reviews the common features of time series data, relevant datasets, and evaluation metrics for models. It also conducts experimental comparisons of various forecasting algorithms, focusing on time and algorithmic architectures. This paper conducts prediction experiments on several deep learning models using the ETT dataset and presents the final results. We evaluate model performance using metrics like Mean Absolute Error (MAE) and Mean Squared Error (MSE). We highlight the strengths and weaknesses of deep learning-based TSF methods. Major deep learning-based time series forecasting methods are introduced and compared. Finally, challenges and future research directions in applying deep learning to time series forecasting are discussed.