# Time Series Forecasting Method Based on Multi-Scale Feature Fusion and Autoformer
## 4 Discussion  
This study proposes a time-series tailored multi-scale feature fusion network and develops the MD-Autoformer model based on an enhanced Autoformer architecture. By integrating multi-scale feature fusion and date-time encoding, the model significantly improves its capability to capture dynamic trends and seasonal patterns in data. Experimental results demonstrate that MD-Autoformer achieves average reductions of 22.23% in RMSE and 29.73% in MAE compared to the baseline Autoformer on the apparel sales dataset. Furthermore, MD-Autoformer exhibits robust predictive performance on public datasets spanning diverse domains including meteorology, finance, and disease epidemiology. Statistical significance analysis (\(p<0.05\)) confirms the validity of the proposed methodology. The following conclusions are drawn from the experiments conducted in this study:  
* RH1: Ablation and comparative experiments demonstrated that incorporating date-time encoding enhanced the model's ability to recognize periodic temporal patterns, thereby supporting RH1. * RH2: The multi-scale feature fusion network improved Autoformer's prediction accuracy by extracting both local and global temporal features. Ablation studies confirmed that this approach effectively reduced error accumulation in scenarios with multi-scale seasonality, supporting RH2. * RH3: Statistical significance analysis (\(p<0.05\)) revealed that combining date-time encoding with the multi-scale fusion network led to a statistically significant improvement in prediction performance, validating RH3. Figure 10: Experimental results of MD–Autoformer after hyperparameter optimization on different datasets. RH4: The TPE-based Bayesian hyperparameter optimization improved the prediction accuracy of MD-Auttoformer across datasets from multiple distinct domains, thereby validating RH4. The experimental results further reveal that the multi-scale feature fusion network based on multi-scale convolutions can effectively extract the key features of time series from different time scales, accurately capturing multi-level information from short-term fluctuations to long-term trends. Meanwhile, the introduction of date-time encoding enables the model to focus on the potential relationships between data and time, thereby enhancing its perception of dynamic trend changes. Combining with the Bayesian hyperparameter optimization method further improves the performance of the model. We think that hybrid models have an edge in scenarios with high data volatility. Auttoformer-based models, focusing on long-term seasonal trends, may generate overly smooth forecast curves, struggling to track sequence changes flexibly. So, we will explore combining simple neural networks with transformer-based models in future research. Based on our experimental results, we recommend using the MD-Auttoformer for time-series forecasting with clear seasonality or periodicity, such as in fashion sales, power demand, and disease prediction. In future work, we will continue to refine our methodology to enable its application in more complex prediction tasks (e.g., economic policies, and market sentiment) and enhance its adaptability to volatile data. This advancement aims to improve the reliability of our model for sophisticated applications such as stock risk prediction. Conceptualization, X.M. and H.Z. ; formal analysis, X.M. ; methodology, X.M. ; resources, X.M. and H.Z. ; supervision, H.Z. ; validation, X.M. ; writing--original draft, X.M. ; writing--review and editing, X.M. and H.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by \"Pioneer\" and \"Leading Goose\" R&D Program of Zhejiang, grant number 2022C01220, 2025C01067. Not applicable. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. This research benefited greatly from the contributions of [PERSON], whose valuable revisions and feedback during the revision stage significantly improved the manuscript.