Clustering time series data
Clustering time series data is a technique used to identify similar patterns within a set of time series. It can be used for a variety of applications such as detecting anomalies, grouping similar behaviors, and segmenting data. In this explanation, I will use the k-means algorithm as an example of how to cluster time series data in Python.
First, we need to prepare our data. For this example, I will use the Daily Minimum Temperatures in Melbourne dataset, which can be loaded directly from the statsmodels library.
import numpy as np
import pandas as pd
from statsmodels.datasets import get_rdataset
# Load the Daily Minimum Temperatures in Melbourne dataset
data = get_rdataset("melbtemp", "MASS").data
ts = pd.Series(data["temp"].values, index=data["date"])
Next, we will need to transform our time series data into a feature space that can be used for clustering. One common method is to use sliding windows to generate a set of feature vectors, where each vector represents a segment of the time series. For this example, we will use a window size of 30, meaning each vector will represent a segment of 30 consecutive days.
# Define the window size
window_size = 30
# Generate a set of feature vectors using sliding windows
X = np.array([ts[i:i+window_size].values for i in range(len(ts)-window_size)])
Now, we can use the k-means algorithm to cluster our data. We will use the KMeans class from the scikit-learn library.
from sklearn.cluster import KMeans
# Define the number of clusters
n_clusters = 4
# Initialize the k-means algorithm with the number of clusters
kmeans = KMeans(n_clusters=n_clusters, random_state=0)
# Fit the algorithm to the data
kmeans.fit(X)
# Get the cluster labels for each data point
labels = kmeans.labels_
Finally, we can visualize the results by plotting the time series in each cluster.
import matplotlib.pyplot as plt
# Plot the time series in each cluster
fig, axs = plt.subplots(n_clusters, figsize=(10, 8))
for i in range(n_clusters):
axs[i].set_title("Cluster " + str(i))
for j in range(len(X)):
if labels[j] == i:
axs[i].plot(X[j])
plt.show()
This will produce a set of plots, where each plot shows the time series in a different cluster.
Clustering time series data can be a useful technique for understanding and analyzing complex datasets. By identifying similar patterns in the data, we can gain insights into the underlying structure and behavior of the system being studied.
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