Filter Time Series data using NumPy and SciPy Signal processing

Last update on March 27 2025 09:13:37 (UTC/GMT +8 hours)

5. Signal Processing on Time Series Data

Write a NumPy program to create a time series dataset and apply SciPy's signal processing functions to filter the data.

Sample Solution:

Python Code:

# Import necessary libraries
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt

# Create a time series dataset using NumPy
np.random.seed(0)  # For reproducibility
time = np.linspace(0, 1, 500, endpoint=False)
data = np.sin(2 * np.pi * 7 * time) + np.random.randn(500) * 0.5

# Design a Butterworth filter using SciPy
b, a = signal.butter(4, 0.1)

# Apply the filter to the time series data
filtered_data = signal.filtfilt(b, a, data)

# Plot the original and filtered data
plt.plot(time, data, label='Original Data')
plt.plot(time, filtered_data, label='Filtered Data', linewidth=2)
plt.legend()
plt.show()

Output:

Explanation:

• Import necessary libraries:
• Import NumPy, SciPy's signal module, and Matplotlib for plotting.
• Create a time series dataset using NumPy:
• Generate time series data consisting of a sine wave with added noise.
• Design a Butterworth filter using SciPy:
• Use SciPy's butter function to design a low-pass Butterworth filter.
• Apply the filter to the time series data:
• Use SciPy's "filtfilt()" function to apply the filter, ensuring zero phase distortion.
• Plot the original and filtered data:
• Use Matplotlib to visualize both the original noisy data and the filtered data.

For more Practice: Solve these Related Problems:

• Write a Numpy program to generate a synthetic time series and apply a low-pass filter using SciPy's signal module.
• Write a Numpy program to simulate a noisy signal and remove noise using a band-stop filter from SciPy's signal processing tools.
• Write a Numpy program to compute and plot the frequency response of a filter designed with SciPy's signal module.
• Write a Numpy program to design and apply a digital filter on a time series, then compare the filtered output with the original signal.

Python-Numpy Code Editor:

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