## 4 Conclusions  
Our research investigated self-interpolation methodologies for monthly time series data, employing both classical and machine learning techniques. A notable discovery emerged: ML methods not originally designed for time series interpolation (ex. CatBoost or Gradient Boost) exhibited high performance. This important efficacy originates primarily from the inherent monthly periodicity, with predictor columns effectively capturing values across diverse monthly contexts. A cornerstone of this research is the development of an innovative self-assessment methodology, enabling the evaluation of imputation methods without reliance on external reference datasets. This approach not only enhances the reliability of gap-filling techniques but also broadens their applicability to regions with limited observational infrastructure. By simulating diverse missingness scenarios and applying systematic validation metrics, the study highlights the critical interplay between accuracy, computational efficiency, and methodological simplicity in selecting optimal imputation strategies. Our simulation analyses revealed the CatBooster method as the most performant approach. However, this superior performance comes with a significant trade-off in computational complexity, particularly when compared to the competitive random forest and Kalman filter methods. Traditional mean absolute error metrics proved insufficient for comprehensive method assessment. We identified critical additional performance indicators: variance of error value series and frequency of extreme values. The research's second phase focused on developing a methodology for assessing interpolation success in scenarios lacking reference datasets. Our breakthrough approach involves the backward interpolation technique, systematic method comparison and context-specific interpolation technique identification  
The research findings offer significant practical solutions for interpolation challenges in real-world scenarios, particularly in environments with missing reference stations, an absence of supplementary reconstructive data sources, and complex temporal data characteristics. The imputation methods described in the article show strong potential for generalization to other fields, particularly those dealing with time series data that exhibit periodic patterns or structural relationships, such as medical monitoring, industrial sensor data, or financial time series, in cases when the probability of a data point being missing is independent of any other observed or unobserved variable. However, successful generalization requires that the data exhibit sufficient internal structure and periodicity, with a low missing data percentage (5-20% as studied), which in most real-world applications is an acceptable amount to impute without severe bias. The novel performance assessment methodology using adjacent time points is especially valuable across disciplines since it addresses the common challenge of evaluating imputation accuracy without complete reference datasets. The article's Performance Index, which combine multiple evaluation metrics, offer a sophisticated framework that could be adapted for quality assessment in different domains. However, researchers would need to carefully evaluate whether their specific data exhibits enough internal structure to support imputation and potentially adjust the evaluation metrics to match their field's requirements. Our study demonstrates the potential of advanced self-interpolation techniques to address critical data reconstruction challenges, providing a robust framework for handling incomplete time series datasets across various environmental and scientific domains. Conceptualization, I.H. and Z.M.-S.; methodology, I.H. and Z.M.-S.; software, Z.M.-S. and A.M.-S.; validation, I.H. and Z.M.-S.; formal analysis, I.H., Z.M.-S. and A.M.-S.; investigation, I.H. and Z.M.-S.; resources, I.H.