# Preptimize: Automation of Time Series Data Preprocessing and Forecasting
## 1 Introduction
Time series data with seasonality pose unique challenges, such as multi-seasonality and heteroscedasticity, which complicate analysis and prediction. Research [23; 24] has addressed these challenges by preprocessing data, building models, and adapting them to original measurements, followed by anomaly detection. Further studies aim to enhance local trend prediction and outlier correction in time series analysis. Non-stationarity has been addressed in studies focusing on volatile and non-volatile data cleaning [25]. Techniques like k-nearest neighbors and sliding windows have been explored [26], with sliding windows performing better, particularly for volatile data like stock market time series [27]. Efforts have been made to measure data quality using indices like the Attribute Value Quality Index (AVQI) and Structured Data Value Quality Index (SDVQI), enabling comparison and improvement of data quality measures. Optimization algorithms like genetic algorithms (GAs) have been employed to automate feature selection in preprocessing, improving regression models' efficiency [28]. While existing solutions like SCREEN, CLEANITS, DAEMON, and Clairvoyance address specific preprocessing challenges, there is a need for a generic approach spanning multiple domains. Preptimage stands out by offering an integrated solution for preprocessing and model recommendation, making it accessible to non-experts and capable of handling diverse time series challenges. SCREEN and DAEMON focus on anomaly detection and cleaning, while CLEANITS specializes in imputation. Clairvoyance is primarily a forecasting tool utilizing advanced machine learning techniques. Preptimage's broad applicability and user-friendly approach differentiate it from these specialized tools. Various open-source libraries and proprietary SaaS products automate machine learning pipeline generation and optimization, streamlining the preprocessing process [29; 30]. Time series values to be forecasted can be nominal, categorical, textual, and numerical. Numerical forecasting is a regression problem which can be solved using various techniques like moving averages, autoregression, and exponential smoothing, as well as advanced methods like ARIMA, SARIMA, VAR, and LSTM [31]. Hybrid approachescombining statistical and machine learning models are also common [32; 33; 34]. However, no single technique suits all time series due to their varying properties, making the selection process time-consuming and complex. To streamline the process of analyzing a time series, a standard procedure should be adopted to automate profiling, preprocessing, optimized model forecasting, evaluation, and selection of the best-performing model with minimal error. The primary contribution of this work is the introduction of Preptimize, an automated strategy for analyzing time series data that integrates statistical and machine learning techniques to recommend optimal models for analysis. By reading a sample from a large dataset, Preptimize suggests blueprint models based on optimization, including the best combination of preprocessing and forecasting techniques, thereby enhancing forecasting accuracy across various types of time series data. Preptimize streamlines the process of analyzing both univariate and multivariate time series with missing values. On basis of the complexity involved in time series analysis, this research highlights the importance of choosing the right forecasting technique and preprocessing strategy based on the type of time series data to expedite the process of analyzing the time series data. Additionally, this work demonstrated the framework's potential for broad applicability in time series forecasting, supporting its effectiveness in recommending suitable prediction models.