Guide for Time Series Data Projects

This comprehensive guide divided into 3 parts covers everything you need to know to get started with a time series data project:

• Part 1 is about Data Modeling, Storage, and Lifecycle with CrateDB.
  
  
• Part 2 explains ingestion, indexing, analysis, and optimization.
  
  
• Part 3 below goes through time series data visualization and advanced analysis through machine learning. Key highlights include: 
  • Employing Grafana for dynamic Time Series data Visualization; 
  • Conducting Exploratory Data Analysis (EDA) to identify data patterns, trends, and outliers; 
  • Applying Time Series Data Decomposition to dissect time series data into trend, seasonality, and irregularities; 
  • Using AutoML for Anomaly Detection to identify significant deviations in datasets over time; 
  • Utilizing AutoML for Time Series Forecasting in decision-making processes across various industries.