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

What is the right way to read a CSV file as a pandas DataFrame?

What is the right way to read a CSV file as a pandas DataFrame?

`df = df[(df['price']>100) | (df['Close']<200)]`

`df = df[(df['price']>100) & (df['Close']<200)]`

How can a DataFrame be filtered to get prices between `100` and `200`?

How can a DataFrame be filtered to get prices between <code>100</code> and <code>200</code>?

How can a `year` column be added to a DataFrame from a `date` column?

How can a <code>year</code> column be added to a DataFrame from a <code>date</code> column?

**(Select all that apply.)** How can we deal with outliers and missing values caused by an error? 

(Select all that apply.) How can we deal with outliers and missing values caused by an error?

`fig, axe = plt.subplots()
axe.plot(df['price'])`

How can we plot a column called `price` as a line chart (knowing that the index of the DataFrame is a `DateTimeIndex`)?

How can we plot a column called <code>price</code> as a line chart (knowing that the index of the DataFrame is a <code>DateTimeIndex</code>)?

Check your understanding of "Python Basics for Time Series."

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: Python Basics for Time Series