Fourier Transformation for Time Series Feature Engineering
Time series data analysis is an essential part of numerous fields, from finance and healthcare to manufacturing and energy. One common challenge in working with time series data is extracting meaningful features that capture underlying patterns and cycles. Fourier Transformation is a powerful technique that can help us achieve this goal.
Understanding Fourier Transformation
Fourier Transformation is a mathematical method used to transform a signal from its original domain (usually time) into a frequency domain. It decomposes a complex waveform into a sum of simpler sine and cosine functions, revealing the underlying frequency components of the signal. In the context of time series data, Fourier Transformation helps us identify the periodic patterns or cycles present in the data.
Purpose of Fourier Transformation in Time Series Analysis
- Identifying Seasonality: Many time series datasets exhibit seasonality, where patterns repeat at regular intervals, such as daily, weekly, or yearly. Fourier Transformation allows us to extract and quantify these seasonal patterns.
- Feature Engineering: By converting time series data into the frequency domain, we can create new features that capture the dominant frequencies or cycles present in the data. These features can be valuable for forecasting.
Implementing Fourier Transformation in Python
I have a briefly walked through the process of using Fourier Transformation for feature engineering for a synthetic data with a practical example using Python and the popular libraries like NumPy, Scipy, Pandas, and Matplotlib in the following colab notebook:
Conclusion
Fourier Transformation helps converting time series data into the frequency domain enabling us to create a new set of features that can be used in machine learning models for more accurate forecast.