# Time Series Forecasting Method Based on Multi-Scale Feature Fusion and Autoformer
## 1 Introduction  
### Literature Review  
For a long time, time series forecasting has been a key research focus in various fields, including business, industry, transportation, finance, and meteorology [1], to understand the underlying trends and patterns of data through data points [2]. Accurate time series forecasting helps enterprises seize market opportunities, prepare for potential challenges in advance, and effectively regulate resource allocation, thereby reducing costs and increasing efficiency [3]. In finance, time series prediction methods strongly support stock price forecasting [4, 5] and macroeconomic indicator forecasting [6], offering references for investors' decision-making [7]. Therefore, time series forecasting is not only an efficient data analysis technique but also an extremely valuable decision-support tool. It plays a vital role in various fields of modern society, and its importance goes without saying. Currently, time series forecasting methods can generally be divided into two categories: statistical methods and machine learning methods [8, 9, 10]. Statistical methods are commonly used for forecasting tasks with lower-complexity time series data, such as ARIMA [11], SARIMA [12], and Grey Model (GM) [13], among others, which are quantitative forecasting methods. These statistical methods are usually simple and easy to implement, making them suitable for medium-and short-term predictions. For instance, in reference [14], the SFOR-ELM model based on data mining effectively predicted e-commerce sales data throughgrey relational analysis and optimization algorithms, thereby improving the efficiency of e-commerce management. Traditional machine learning methods, when combined with statistical approaches, can be applied to small-scale, rapid-forecasting tasks. For example, [PERSON] et al. [15] proposed a Bass model for rapid apparel demand forecasting based on the similarity of clothing and consumer preferences, combined with statistical principles, providing a reference for apparel sales forecasting. However, the main bottlenecks of such research are manifested in two aspects: First, while traditional statistical methods perform reasonably well on low-complexity time series datasets, in practical time series forecasting tasks, the target sequences are often influenced by many exogenous factors such as seasonal trends. Traditional statistical-based methods struggle to achieve precise modeling [16]. Second, the above-mentioned methods have the insufficient capability to extract implicit features from data, making it difficult to uncover long-term cyclical patterns embedded in the data. They are also susceptible to short-term fluctuations and noise, which can lead to larger prediction errors and consequently affect corporate inventory management and sales. With the development of machine learning, researchers have proposed hybrid models like GRU-Prophet [17], SARIMA-CNN-LSTM [18], and ARIMA-LSTM [19; 20; 21] to overcome the limitations of traditional statistical methods and improve time series forecasting accuracy. These models combine machine learning and statistical methods to capture time series information more comprehensively and have been widely applied in practice. In deep learning research, the authors of [22] presented the Autoformer model based on the Transformer, innovatively developing the self-correlation attention mechanism for periodic feature extraction and adaptive frequency component selection in the frequency domain. Many scholars have improved and applied the Autoformer. The authors of [23] combined a genetic algorithm and an elite voting operator to create the GA-Autoformer model, which significantly enhanced pollution prediction accuracy in smart city management. Literature [24] built a hybrid ECA-CAE and Autoformer architecture, achieving high-precision life-span prediction for mechanical systems even with incomplete degradation data.