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

**(Select all that apply.)** Which of the following are evaluation metrics? 



(Select all that apply.) Which of the following are evaluation metrics?

What does a greater R-squared usually mean? 

What does a greater R-squared usually mean?

Splitting on a date and then using older data to train and newer data to test.

Using a sliding window for models that need recent data.

**(Select all that apply.)** How is a train/test split with time series data made? 



(Select all that apply.) How is a train/test split with time series data made?

**(Select all that apply.)** Which of the following can we use as baselines for model evaluation? 


(Select all that apply.) Which of the following can we use as baselines for model evaluation?

The difference in model performance between the train and test sets.

A decay in model performance as a result of changes in context.

Check your understanding of the "Forecasting Evaluation" 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: Forecast Evaluation