In this post, we'll learn a Complete Guide to Time Series Forecasting using ARIMA
ARIMA is one of the most popular satistical models. It stands for AutoRegressive Integrated Moving Average and it’s fitted to time series data either for forecasting or to better understand the data. We will not cover the whole theory behind the ARIMA model but we will show you what’s the steps you need to follow to apply it correctly.
They key aspects of ARIMA model are the following:
The ARIMA model can be applied when we have seasonal or non-seasonal data. The difference is that when we have seasonal data we need to add some more parameters to the model.
For non-seasonal data the parameters are:
For seasonal data we need to add also the following:
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In this post, we;ll learn Time Series Decomposition in Python using Statsmodels.
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