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Guide for Time Series Data Projects

Part 3: Visualization, Exploratory Data Analysis, Forecasting, and Anomaly Detection

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. 

 

Guide for Time Series Data Projects Part 3: Visualization, Exploratory Data Analysis, Forecasting, and Anamoly Detection

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