What are the pros and cons of different clustering algorithms for time series data?

1 Distance-based methods

Distance-based methods use a predefined metric to calculate the similarity or dissimilarity between pairs of time series. The most common metric is the Euclidean distance, which measures the straight-line distance between two points. However, this metric can be sensitive to noise, shifts, and scaling in the data. Other metrics, such as dynamic time warping (DTW), can account for these variations by aligning the time series and finding the optimal match. The advantage of distance-based methods is that they are simple and intuitive, and can work with any clustering algorithm that requires a distance matrix, such as k-means or hierarchical clustering. The disadvantage is that they can be computationally expensive, especially for long or high-dimensional time series, and they may not capture complex or nonlinear relationships in the data.

2 Feature-based methods

Feature-based methods transform the time series into a lower-dimensional space by extracting relevant features that describe their characteristics, such as mean, variance, frequency, trend, seasonality, or shape. The features can be either domain-specific, based on prior knowledge or expert input, or domain-agnostic, based on generic methods such as principal component analysis (PCA) or discrete Fourier transform (DFT). The advantage of feature-based methods is that they can reduce the dimensionality and complexity of the data, and make the clustering more interpretable and robust. The disadvantage is that they may lose some information or introduce noise in the feature extraction process, and they may require domain knowledge or trial-and-error to select the appropriate features.

3 Model-based methods

Model-based methods fit a probabilistic model to each time series, and cluster them based on the similarity or dissimilarity of their model parameters or likelihoods. The models can be either parametric, such as autoregressive (AR) or moving average (MA) models, or nonparametric, such as hidden Markov models (HMM) or Gaussian processes (GP). The advantage of model-based methods is that they can capture the underlying dynamics and dependencies of the time series, and handle uncertainty and missing values. The disadvantage is that they can be more complex and difficult to implement, and they may require assumptions or prior knowledge about the data distribution or the number of clusters.

4 Hybrid methods

Hybrid methods combine two or more of the above methods to leverage their strengths and overcome their weaknesses. For example, one can use feature extraction to reduce the dimensionality of the data, and then use distance-based or model-based methods to cluster the features. Alternatively, one can use model-based methods to generate synthetic time series, and then use distance-based or feature-based methods to cluster the original and synthetic time series. The advantage of hybrid methods is that they can improve the accuracy and efficiency of the clustering, and handle different types of time series. The disadvantage is that they can be more complicated and challenging to design, and they may introduce additional parameters or trade-offs.

5 How to choose a clustering algorithm

When selecting a clustering algorithm for time series data, there is no one-size-fits-all solution. The choice depends on factors like the goal and context of the analysis, the nature and quality of the data, the available computational resources and time, and the evaluation criteria and validation methods. Generally, if you have a large or high-dimensional dataset, feature-based or hybrid methods may reduce complexity and improve scalability. If you have a noisy or heterogeneous dataset, distance-based methods with robust metrics (e.g. DTW) or model-based methods with flexible models (e.g. HMM or GP) may be better suited. If you have a domain-specific or interpretable dataset, feature-based methods with relevant features or model-based methods with appropriate models (e.g. AR or MA) may be suitable. For complex or nonlinear datasets, model-based or hybrid methods may be more effective in capturing dynamics and relationships in the data. If you have an exploratory or unsupervised dataset, distance-based or feature-based methods that do not require prior knowledge about the data or number of clusters are recommended. Finally, for supervised or semi-supervised datasets, model-based or hybrid methods that can incorporate prior information into clustering may be best suited.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?