# Preptimize: Automation of Time Series Data Preprocessing and Forecasting
## 1 Introduction  
Time series data, characterized by sequential data points recorded at specific timestamps, present unique challenges in data science, demanding specialized preprocessing techniques to ensure data quality. Time series analysis encompasses data collection, cleaning, preprocessing, and forecasting, tailored to different types of time series, whether numeric or textual, univariate or multivariate, seasonal or nonseasonal. Existing research predominantly focuses on univariate time series, while multivariate time series pose additional complexities such as noise, missing values, outliers, and seasonality. To address these challenges, an automated preprocessing framework integrating various techniques and machine learning algorithms is proposed. Time series analysis, crucial in IoT and financial domains, requires extensive data cleaning and preprocessing due to its distinctive characteristics and challenges. Understanding the properties of the data, such as stationarity and distribution, is essential for selecting appropriate preprocessing techniques. Additionally, deep learning algorithms like GANs and auto-encoders show promising results in preprocessing and forecasting time series data. The quest for the most accurate forecasting model with minimal error rates in numerical time series analysis requires exhaustive experimentation with various techniques and their combinations. The type of technique applicable to a particular type of data should also be known [1]. However, this process is time-consuming and labor-intensive. Different imputation methods are employed based on the time series properties, such as distribution and seasonality, to handle missing values effectively. With the exponential growth of time series data generated by diverse sources like stock exchanges [2], energy consumption [3], health monitoring systems, and weather recording systems, analysts face increasing workloads. Time series, characterized by sequential data recorded over time intervals, present unique challenges such as handling noise, seasonality, and missing values, which necessitate specialized preprocessing techniques to ensure data quality and improve forecasting accuracy. Preprocessing, a crucial step in data analysis and machine learning, involves a series of steps to enhance data quality and simplify model training. However, preprocessing is time-intensive, lacks a predefined sequence, and varies across different domains. In the context of time series analysis, preprocessing becomes more complex due to the real-time nature and sequential dependencies of the data. Challenges include handling missing, duplicate, and incorrect data, including outliers. The existing literature predominantly focuses on handling missing data and outlier detection in time series preprocessing. Outliers, which deviate significantly from the majority of data points, can be univariate or multivariate, with the latter posing greater complexity. Multivariate outlier detection often requires machine learning approaches due to the multidimensional feature space involved. Outlier detection techniques encompass statistical and machine learning methods. Statistical techniques include exponential smoothing, z-score, linear regression models, and interquartile range, while machine learning techniques comprise supervised methods like neural networks and decision trees, and unsupervised methods like cluster analysis. Distance measuring techniques, information theory-based methods, and algorithms like KNN and COF are also utilized. Handling missing values in time series data involves either removing incomplete records or imputing missing values. Imputation techniques for time series data, including spline interpolation and moving averages, differ from those used in non-time series data due to temporal properties. Traditional methods may not suffice, necessitating advanced techniques like interpolation, auto-encoders, and generative adversarial networks (GANs) for imputation and outlier detection.