Advanced time series analysis

Learn how to conduct advanced data analysis on large time series datasets with CrateDB.

Anomaly detection Forecasting / Prediction Time series decomposition Exploratory data analysis

Anomaly detection and forecasting

To gain insights from your data in a one-shot or recurring way, based on machine learning techniques, you may want to look into applying anomaly detection and/or forecasting methods.

Examples

Use MLflow for time series anomaly detection and time series forecasting

Guidelines and runnable code to get started with MLflow and CrateDB, exercising time series anomaly detection and time series forecasting / prediction using NumPy, Merlion, and Matplotlib.

Anomaly Detection Forecasting / Prediction

Python MLflow

Use PyCaret to train time series forecasting models

This notebook explores the PyCaret framework and shows how to use it to train various time series forecasting models.

Forecasting / Prediction

Python PyCaret MLflow

Time series decomposition

Decomposition of time series is a statistical task that deconstructs a time series into several components, each representing one of the underlying categories of patterns.

There are two principal types of decomposition, one based on rates of change, the other based on predictability.

You can use this method to dissect a time series into multiple components, typically including trend, seasonal, and random (or irregular) components.

This process helps in understanding the underlying patterns of the time series data, such as identifying any long term direction (trend), recurring patterns at fixed intervals (seasonality), and randomness (irregular fluctuations) in the data.

Decomposition is crucial for analyzing how these components change over time, improving forecasts, and developing strategies for addressing each element effectively.

Examples

Analyze trend, seasonality, and fluctuations with PyCaret and CrateDB

Learn how to extract data from CrateDB for analysis in PyCaret, how to further preprocess it and how to use PyCaret to plot time series decomposition by breaking it down into its basic components: trend, seasonality, and residual (or irregular) fluctuations.

Time series decomposition

Python PyCaret

Exploratory data analysis (EDA)

Exploratory data analysis (EDA) is an approach of analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods.

EDA involves visualizing, summarizing, and analyzing data, to uncover patterns, anomalies, or relationships within the dataset.

The objective of this step is to gain an understanding and intuition of the data, identify potential issues, and, in machine learning, guide feature engineering and model building.

Examples

Exploratory data analysis (EDA) with PyCaret and CrateDB

Learn how to access time series data from CrateDB using SQL, and how to apply exploratory data analysis (EDA) with PyCaret.

The notebook shows how to generate various plots and charts for EDA, helping you to understand data distributions, relationships between variables, and to identify patterns.

EDA on time series

Python PyCaret