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Course overview 00:04:24
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Course notebooks
- Foundation of Time Series Data
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Foundation of Time Series Data 00:07:19
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Time Series Basic Functions 00:05:14
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Stationary Data 00:09:53
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Stationary Data Continued 00:06:09
- Visualization of Time Series
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Line Plots and Seasonal Decomposition Plots 00:09:04
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Autocorrelation and Partial Autocorrelation Plots 00:04:20
- Time Series Forecasting
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Forecasting: Modeling Setup 00:04:58
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Autoregressive (AR) Models 00:11:15
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Moving Average (MA) Models 00:14:08
- Model Evaluation and Validation
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Training Data 00:05:12
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Testing Data 00:06:50
- Real-world Time Series Analysis
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Time Series Prediction Example 00:04:41
- Conclusion
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Conclusion 00:01:05
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End of Course Survey
Time Series in Python
Utilizing time-series data for predictive analytics.
Time-series data, or data collected over time, is prevalent across many fields, from finance to medicine and from meteorology to e-commerce. Whether you're forecasting stock prices, analyzing patterns in climate change, or predicting sales, understanding time-series data is an essential skill in today's data-driven world. In this course, you’ll gain comprehensive insights into handling, analyzing, and forecasting time-series data using Python. Leveraging powerful Python libraries such as pandas and statsmodels, you’ll be equipped with the tools and techniques to derive meaningful insights from sequential data and predict future values.
By the end of this hands-on course, you’ll understand:
- Unique characteristics of time-series data and why it is distinct from other data types.
- Importance of stationarity (i.e., data that does not depend on a specific time period) in time-series analysis and techniques to achieve it.
- Basics of time-series forecasting models in Python, including autoregressive (AR), moving average (MA), autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average (SARIMA), and when to use them.
And you’ll be able to:
- Load, manipulate, and handle missing values in time-series data using Python and pandas.
- Effectively visualize time-series data using line plots, seasonal decomposition plots, and correlation plots.
- Build, evaluate, and validate time-series forecasting models using real-world datasets.
This training is for you because…
- You’re a student or experienced data scientist looking to learn more about analyzing time-series data.
- You work with sequential and/or temporal datasets.
- You want to become an expert in time-series forecasting and analysis.
Prerequisites
- Proficiency with basic Python programming
- Knowledge of fundamental statistics (basic data analysis, summary statistics, such as variance, linear regression, and hypothesis testing)
Recommended preparation
- Python First Steps course
- Introduction to Python Programming Learning Path
- Linear Algebra course
- Statistics and Hypothesis Testing course
About the instructor
Blake Rayfield is an Associate Professor of Finance at Northern Arizona University, with a demonstrated history of working in the higher education industry. He holds an M.S. in Financial Economics and a Ph.D. in Financial Economics from The University of New Orleans. He has published in several peer-reviewed journals, including the Journal of Financial Research, Quarterly Review of Economics and Finance, and the Review of Behavioral Finance, among others. His research interests are in Corporate Finance and Investments, and he incorporates Python and data visualization in all projects. You can find him here: LinkedIn | GitHub | ResearchGate.
Questions? Issues? Contact [email protected].