Introducing time series forecasting in Python: the Random walk forecast

Check out Marco Peixeiro's book 📖 Time Series Forecasting in Python | mng.bz/95Mr 📖 To save 40% on Marco's book use the DISCOUNT CODE ⭐ watchpeixeiro40 ⭐ Join Marco in this introductory lesson on time series forecasting in Python. Marco explores the random walk model, MA(q) and AR(p) models., as well as the foundational concept of stationarity, and how to use the ACF and PACF plots for forecasting.
Watch the full video at: mng.bz/J20Z
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Time Series Forecasting in Python | mng.bz/95Mr
To save 40% off this book use discount code: watchpeixeiro40
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About the book:
Time Series Forecasting in Python teaches you to apply time series forecasting and get immediate, meaningful predictions. You’ll learn both traditional statistical and new deep learning models for time series forecasting, all fully illustrated with Python source code. Test your skills with hands-on projects for forecasting air travel, volume of drug prescriptions, and the earnings of Johnson & Johnson. By the time you’re done, you’ll be ready to build accurate and insightful forecasting models with tools from the Python ecosystem.

[@Janamejaya.Channegowda]

Thank you for sharing.

[@ManningPublications]

Glad you liked it. :)

[@karthikb.s.k.4486]

Thank you. Does this book includes DEEP AR and prophet also?

[@datasciencewithmarco]

It does include Prophet, but not Deep AR!