From the course: Python for Time Series Forecasting

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Introduction to developing ARIMA models

Introduction to developing ARIMA models - Python Tutorial

From the course: Python for Time Series Forecasting

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Introduction to developing ARIMA models

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- [Instructor] Statistical forecasting with ARIMA. In this notebook, I'll show you how to fit the mathematical equation of the ARIMA model that we can use to forecast the future based on the historical patterns. At the time we fitted the mathematical equation, you can observe that we have put some parameters, specifically p, d and q, which stand for the AutoRegressive, Integrated and Moving Average part of the model. Not only you will learn how to choose the appropriate ones based on the analysis from the differencing, the partial autocorrelation function and the auto correlation function, but also you will visualize different configurations in the playground to see which ones are better to visualize the forecast of this ARIMA model. And finally, you cannot be happy with having good forecasts visually. You must validate the assumptions through the diagnostics. And these are the autocorrelation of the residuals, the normality, and the homoscedasticity. Before jumping into the next…

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