ARIMA and Fourier Transform for Time Series Forecasting: A Python-Based Approach

The ability to forecast future trends based on historical data is crucial in various fields such as finance, weather forecasting and sales predictions. In the realm of time series forecasting, techniques like ARIMA (Autoregressive Integrated Moving Average) and Fourier Transform play a significant role in analyzing and predicting patterns within time-dependent data. Implementing these techniques in Python offers a flexible and powerful environment to explore, model and forecast time series data efficiently.

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Table of Contents

• Section 1: Understanding Time Series Data: Explore the characteristics of time series data and how to manipulate it using Python libraries such as Pandas.
• Section 2: ARIMA Model: Explain the components of the ARIMA model and how to implement it for time series forecasting in Python using the statsmodels library.
• Section 3: Fourier Transform: Introduce the Fourier Transform and how it can be used to analyze the frequency components of a time series in Python using the numpy library.
• Section 4: Combining ARIMA and Fourier Transform: Show how ARIMA and Fourier Transform can be combined to improve time series forecasting accuracy in Python.
• Section 5: Evaluating…