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Time Series Classification in Python
Rating: 4.7 out of 5(15 ratings)
174 students

Time Series Classification in Python

Develop robust and performant classification models for time series data using machine learning and deep learning
Created byMarco Peixeiro
Last updated 1/2025
English
English [Auto],

What you'll learn

  • Build optimized time series classification models
  • Gain a deep understanding of algorithms and how they work
  • Use machine learning and deep learning for time series classification
  • Visualize complex time series classification data
  • Gain experience with real-life datasets in healthcare, IoT, spectroscopy and more!

Course content

11 sections • 57 lectures • 6h 34m total length

  • Introduction to time series classification4:53

    Discover time series classification, a supervised task using time-based features to assign categories, with applications in equipment monitoring, healthcare ecg, and speech recognition, across distance-based to deep learning methods.

  • Set up your environment for coding2:29

    I recommend using Anaconda or Miniconda to manage your virtual environments.

  • Course notes and model reference0:19
  • Code - Baseline models14:55

Requirements

  • Familiarity with Python
  • Knowledge of common machine learning concepts like: train/test split, grid search.

Description

Master time series classification in Python! This course covers machine learning and deep learning techniques for classifying time series, all applied in guided hands-on projects in 100% Python.


By the end of this course, you will:

  • master time series classification

  • perform feature engineering and model optimization for classification

  • learn and implement state-of-the-art machine learning and deep learning models

  • get hands-on experience with real-life datasets in the fields of healthcare, IoT, sensor data, spectroscopy and more

This is the most complete course on time series classification! We cover all types of models like:

  • Distance-based

  • Dictionary-based

  • Ensemble models

  • Feature-based

  • Interval-based

  • Kernel-based

  • Shapelet models

  • Meta classifiers

We first explore the theory and inner workings of each model before applying them in a hands-on project using Python.

Plus, get an additional section covering deep learning models, giving you a blueprint to apply any deep learning architecture for time series classification. All functions are flexible such that you can handle series with any number of features, samples and time steps.


Detailed outline:

  • Introduction to time series classification

    • Application of time series classification

    • Baseline classifiers

  • Distance-based method

    • Euclidean distance

    • K-Nearest Neighbors classifier

    • Dynamic Time Warping (DTW) from scratch

    • ShapeDTW

  • Dictionary-based models

    • BOSS

    • WEASEL

    • TDE

    • MUSE

    • Capstone project: Japanese vowels' speakers classification

  • Ensemble methods

    • Bagging

    • Weighted classifier

    • Time series forest

  • Feature-based methods

    • Summary classifier

    • Matrix profile

    • Catch22

    • TSFresh

    • Capstone project: Classify equipment failure in a processing plant

  • Interval-based method

    • RISE

    • CIF

    • DrCIF

  • Kernel-based methods

    • Support vector machine

    • Rocket

    • Arsenal

    • Capstone project: Classify appliances by their electricity usage

  • Shapelet-based methods

    • Shapelet transform classifier

  • Hybrid models

    • HIVE-COTE

    • Capstone project: Beverage classification through spectroscopy

  • EXTRA: Deep learning for time series classification

In this module, we develop a blueprint such that you can apply any deep learning architectures for time series classification. By the end, you will have built flexible functions that can adapt to series with any number of samples, features and time steps.

  • Deep learning blueprint with Keras

  • Deep learning blueprint with PyTorch

Who this course is for:

  • Data scientists working with in healthcare, IoT, or equipment monitoring through sensor data
  • Practitioners who want to develop state-of-the-art classification models

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