<ul><li>Time series data is often analyzed in the time domain, but many systems exhibit patterns that are more easily understood in the frequency domain.</li><li>The Fourier transform is a crucial tool in frequency domain analysis, allowing the conversion of a time-domain signal into its constituent frequencies.</li><li>Applying the Fourier transform in time series analysis helps identify dominant cycles, remove noise, extract features, and detect anomalies or structural breaks.</li><li>This article explores the concept of Fourier transform, its implementation in Python, and its application in revealing hidden structures in real-world time series data.</li></ul>

Understanding the role of Fourier transforms in time series analysis

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