Gain insights into time series analysis using Python. Explore pandas and NumPy for data manipulation, visualize trends, learn ARIMA modeling, and employ machine learning for forecasting.

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time_series

This course is an introduction to time series data analysis and forecasting with Python. Time series data is prevalent in many fields, including finance, economics, and meteorology. In this course, you will learn how to use Python's popular pandas and NumPy libraries to manipulate, visualize, and analyze time series data.

The course covers topics such as time series decomposition, stationary and non-stationary data, autocorrelation and partial autocorrelation, and modeling techniques like ARIMA. You will learn how to implement these techniques in Python and how to use them to make forecasts. Moreover, you will also be introduced to some advanced techniques including machine learning algorithms.

By the end of the course, you will have a solid understanding of time series data analysis and forecasting with Python. You will be able to import, clean, and manipulate time series data, use statistical modeling techniques to make forecasts, and apply machine learning algorithms to time series data.

Time Series Analysis with Python

A measure of the linear relationship between a numeric variable and itself at another point in time.

A measure of the linear relationship between two variables.

A measure of the linear relationship between a numeric variable and itself at the same point in time.

What function(s) can be used to calculate trend and seasonality in a time series?

What function(s) can be used to calculate trend and seasonality in a time series?

What function is used to plot an autocorrelogram?

What function is used to plot an autocorrelogram?

What function is used to test for stationarity?

What function is used to test for stationarity?

If it's under 0.05, assume the series is not stationary.

If it's under 0.05, assume the series is stationary.

If it's under 0.99, assume the series is stationary.

How is the p-value interpreted from an Augmented Dickey-Fuller test?

How is the p-value interpreted from an Augmented Dickey-Fuller test?

Check your understanding of the "Time Series Analysis" chapter.

Introduction to Time Series

Python Basics for Time Series

Time Series Analysis

Basic Time Series Forecasting

Advanced Time Series Forecasting

Forecast Evaluation

Time Series Analysis and Forecasting

Practical Examples

Final Project

Conclusion

Quiz: Time Series Analysis