The Complete Introduction to Time Series Classification in Python

Time series data is ubiquitous across numerous industries, yet while time series forecasting receives considerable attention, time series classification is often overlooked. This article provides a comprehensive introduction to time series classification, exploring its real-world applications, reviewing various methods, and demonstrating some of these techniques in a Python-based classification project. Let's begin!

Understanding Time Series Classification

Time series classification is a supervised machine learning technique where one or more features, measured over time, are used to assign a category. The objective is to label the time series rather than predict future values.

Real-World Applications of Time Series Classification

Time series classification finds extensive use, particularly with sensor data. Key applications include:

• Predictive Maintenance: Monitoring equipment to anticipate potential failures.
• Healthcare: Analyzing electrocardiogram (ECG) data to assess patient health.
• Speech Recognition: Identifying spoken words and speakers from sound wave patterns.
• Food Spectroscopy: Determining alcohol content or identifying food components from spectroscopy data.
• Cybersecurity: Detecting anomalous activity indicative of fraud or breaches.

These diverse applications highlight the importance of time series classification in various fields.

Overview of Time Series Classification Models

Numerous approaches exist for time series classification. This section offers a brief overview of each, with more detailed explanations available in this dedicated guide [link to guide, if available].

1. Distance-Based Models: These models utilize distance metrics (e.g., Euclidean distance) to classify samples. Dynamic Time Warping (DTW) offers a more robust approach, accommodating series of varying lengths and handling slightly out-of-phase patterns. Examples include K-nearest neighbors (KNN) and ShapeDTW.

2. Dictionary-Based Models: These models encode series patterns using symbols and leverage symbol frequency for classification. Examples include BOSS, WEASEL, TDE, and MUSE.

3. Ensemble Methods: These aren't models themselves but rather frameworks combining multiple base estimators for improved prediction. A key advantage is their ability to handle multivariate data using univariate models (e.g., bagging). Examples include bagging, weighted ensemble, and time series forest.

4. Feature-Based Methods: These methods extract features from time series (e.g., summary statistics, Catch22, matrix profile, TSFresh) which are then used to train a classifier.

5. Interval-Based Models: These extract multiple intervals from time series, compute features using methods mentioned above, and then train a classifier. Examples include RISE, CIF, and DrCIF.

6. Kernel-Based Models: These models employ kernel functions to map time series to a higher-dimensional space for easier classification. Examples include Support Vector Classifier (SVC), Rocket, and Arsenal (an ensemble of Rocket).

7. Shapelet Classifier: This classifier identifies and utilizes shapelets (discriminative subsequences) for classification based on distance comparisons.

8. Meta Classifiers: These combine various methods for robust classification performance. HIVE-COTE, combining TDE, Shapelet, DrCIF, and Arsenal, is an example, although it's computationally expensive.

The choice of method depends on factors like data characteristics, computational resources, and desired accuracy.

Hands-On Time Series Classification Project (Python)

This section applies some of the aforementioned techniques to the BasicMotions dataset [link to dataset], comprising accelerometer and gyroscope data from individuals performing various activities (standing, walking, running, badminton).

Setup:

<code class="language-python">import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

from sktime.datasets import load_basic_motions
from sklearn.model_selection import GridSearchCV, KFold</code>

Data Loading:

<code class="language-python">X_train, y_train = load_basic_motions(split='train', return_type='numpy3D')
X_test, y_test = load_basic_motions(split='test', return_type='numpy3D')</code>

Data Visualization (example comparing walking and badminton):

<code class="language-python"># ... (Visualization code as provided in the original article) ...</code>

KNN Classification:

<code class="language-python"># ... (KNN code as provided in the original article) ...</code>

Bagging with WEASEL:

<code class="language-python"># ... (Bagging with WEASEL code as provided in the original article) ...</code>

Evaluation:

<code class="language-python"># ... (Evaluation code as provided in the original article) ...</code>

Conclusion

This article provided an introduction to time series classification, covering its applications and various methods. The practical project demonstrated the application of KNN and bagging with WEASEL. Further exploration of this field is encouraged.

Next Steps

To continue learning, consider exploring the resources mentioned in the original article, including a guide on time series classification methods and a course on the subject.

References

• BasicMotions dataset — [link to dataset]
• Sktime — [link to sktime]

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