Abstract: In multi-process lithium battery manufacturing, data analysis faces challenges of multi-source heterogeneity and missing data. To address these issues, this article proposes a filling method that combines autoregressive integrated moving average model (ARIMA) with interpolation techniques. The proposed method extracts trends and periodic features of time series data through ARIMA models, and integrates interpolation techniques to repair missing values caused by equipment failures or incomplete data collection, enhancing the ability to capture complex data change patterns. Multiple experiments have shown that the proposed ARIMA-interpolation method outperforms traditional techniques such as mean filling, K-nearest neighbor filling, and standalone interpolation in terms of filling accuracy and data integrity. The results confirm that the proposed method effectively improves the quality of data preprocessing in lithium battery manufacturing, providing a reliable data foundation for subsequent feature extraction and analysis.

Key words: missing value filling, data preprocessing, multi-process production, autoregressive integrated moving average (ARIMA)