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ARIMA Models in Python
4.9(409)25k studentsADVANCEDEnglish
Korshub's take
The Korshub Take
This advanced Python course delivers a comprehensive dive into time series forecasting using ARIMA models. Beginning with stationarity tests and ARMA fundamentals, it progresses to ACF/PACF model selection, seasonal SARIMA extensions, and real-world forecasting challenges.
Who It's For
Advanced data scientists, quantitative analysts, and Python developers needing robust statistical techniques to forecast stock prices, trends, and seasonal time series.
Key Takeaways
- Stationarity & ARMA Basics: Test time series stationarity visually and statistically, generating and fitting ARMA models using Statsmodels.
- Model Selection & Diagnostics: Utilize ACF and PACF plots alongside AIC and BIC metrics to select optimal ARIMA model orders.
- Seasonal SARIMA Models: Decompose time series data into seasonal components and fit SARIMA models for complex forecasting challenges.
- Dynamic Predictions: Generate one-step-ahead and dynamic forecasts for financial and trend datasets.
About this course
Have you ever tried to predict the future? What lies ahead is a mystery that is usually only solved by waiting. In this course, you can stop waiting and dive into the world of time series modeling using ARIMA models in Python to forecast the future.
Making these concepts easy to grasp and apply, you’ll uncover generating one-step-ahead predictions, dynamic forecasts, and fitting ARIMA models directly to your data.
This final project ties everything together, giving you a comprehensive understanding of ARIMA modeling.
Time series data
Start by learning the basics of time series data, including the concept of stationarity—crucial for working with ARMA models. You'll learn how to test for stationarity both visually and statistically, generate ARMA data, and fit ARMA models to get a solid foundation.Statsmodels package
As you progress, explore the powerful Statsmodels package for fitting ARMA, ARIMA, and ARMAX models. You'll get hands-on experience using your models to predict future values like stock prices.Making these concepts easy to grasp and apply, you’ll uncover generating one-step-ahead predictions, dynamic forecasts, and fitting ARIMA models directly to your data.
ACF and PACF plots
One of the highlights is learning how to choose the best model using ACF and PACF plots to identify promising model orders. You'll learn about criteria like AIC and BIC for model selection and diagnostics, helping you refine your models to perfection.SARIMA models
The course wraps up with seasonal ARIMA (SARIMA) models, perfect for handling data with seasonal patterns. You'll learn to decompose time series data into seasonal and non-seasonal components and apply your ARIMA skills in a global forecast challenge.This final project ties everything together, giving you a comprehensive understanding of ARIMA modeling.
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