# Preptimize: Automation of Time Series Data Preprocessing and Forecasting
## 1 Introduction
Time series data analysis considers properties such as seasonality and trend, categorizing time series into types based on their characteristics. Challenges in time series data extend beyond outliers and missing values to include concept drift, class imbalance, handling trend, and seasonality and data consistency issues. According to [4], time series analysis is mostly performed using statistical methods; however, machine learning techniques have increasingly gained prominence in time series analysis, promising more accurate results with adequate preprocessing. However, preprocessing remains vital for enhancing data quality and ensuring reliable predictions [5]. Techniques like SCREEN [6] offer constraint-based cleaning methods for time series data, improving accuracy and efficiency in data analysis. Research studies have explored various approaches to enhance preprocessing techniques for time series data. One study [7] utilized meta-learning to select preprocessing alternatives, focusing on transformation effects on algorithm performance across datasets such as Automobile, Credit, Iris, and Vote. While regression trees showed limited effectiveness as a meta-learner, transformations significantly improved outcomes. Another study [8] introduced an iterative minimum repairing and parameter prediction method for anomaly repair, achieving a substantial enhancement in execution speed across datasets including GPS, Intel Lab Data, and China Meteorological data. In a separate investigation [9], Landsat time series imagery combined with trajectory analysis was employed to detect forest instabilities in Myanmar. This study highlighted the correlation between areas of instabilities and harvested trees, indicatingpotential applications for selective logging. Change detection in time series was comprehensively analyzed [10], covering preprocessing techniques like atmospheric correction and cloud detection, along with popular automated preprocessing algorithms like LEDAPS and Fmask. Augmenting fully convolutional networks with LSTM-RNN was proposed [11] for time series classification, demonstrating improved performance with attention mechanisms. Additionally, studies [12] addressed data profiling issues, proposing visual-based software solutions for data quality checks and outlier detection techniques using methods like the k-sliding window technique and neural networks [13]. A review [14] highlighted the need for further research in time series cleaning tools and systems, emphasizing the importance of preprocessing for maximizing data potential. Potential areas for future work include error data type analysis, multidimensional time series cleaning algorithms, anomaly detection, and specific application data cleaning techniques. Several innovative systems have been proposed to address challenges in cleaning time series data across various domains. Cleanits [15], for instance, focuses on industrial time series data cleaning, utilizing Bollinger bands for anomaly detection and LSTM for repairing errors. While primarily designed for industrial data, there is potential for its application in other domains, requiring flexibility enhancements. In the energy sector, knowledge extraction systems have been proposed [16] using clustering techniques, with k-Medoids showing promising results on energy time series. Medical science also benefits from time series data analysis, prompting the development of systems like Clairvoyance [17], facilitating rapid prototyping and optimization of clinical decision systems. Hybrid models and systems like AutoMTS have been introduced for cleaning multivariate time series data [18; 19], demonstrating their significance in improving data quality through imputation and outlier detection. AutoML and frameworks like DAEMON [20; 21] focus on automating machine learning model fitting and outlier detection in time series data, achieving high accuracy and demonstrating potential for online anomaly detection services [22].