Learn Time Series Forecasting in Python
Learn Time Series Forecasting in Python 관련
Learning about time series forecasting in Python is important because it can help predict future trends.
We just posted a course on the freeCodeCamp.org YouTube channel that is an introduction to time series forecasting with Python. You’ll learn what time series data is and how to break it down into its key components like trend, seasonality, and residuals. You'll start by building simple baseline models before learning about powerful forecasting techniques such as ARIMA and seasonal ARIMA. You’ll discover how to forecast future values, evaluate your models using cross-validation, and incorporate exogenous features to improve predictions.
The course also covers how to generate prediction intervals and select the most appropriate evaluation metrics for your projects. You'll gain a clear understanding of the main forecasting techniques and how to apply them in practice.
Marco Peixeiro teaches this course. He is the author of the book Time Series Forecasting in Python from Manning Publications.
This video is the perfect starting point for beginners looking to forecast time series data. The course uses 100% Python code to cover the fundamental concepts of time series forecasting, including:
- defining time series data
- time series decomposition
- forecasting with ARIMA
- cross-validation in time series
- using exogenous features
- generating prediction intervals
- evaluation metrics for forecasting models
Watch the full course on the freeCodeCamp.org YouTube channel (1.5-hour watch).