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This course is an introduction into time series forecasting with Python. Time series forecasting is the use of a model to predict future values based on previously observed values or other relevant types of data. The lessons that are presented here focus not only on classical models such as ARIMA but also state-of-the-art models such as Prophet and tRNNs and tLSTMs. After this course, you will be able to predict with confidence time series such as sales, growth rates or number of visitors. As requirements, experience with python should be enough.
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The Time Series Forecasting with Python training takes 1 day.
The training starts with defining the time series forecasting problem. Then, we will talk about stationarity, a property that is prerequisite for some models, and learn how to test for it. We will then discuss transformations necessary to do before modelling and ACF, PACF plots to help decide what model to use. We will also learn about the STL decomposition, ETS, ARIMA, STL-ARIMA forecasting models and how we can evaluate our models. Finally, we will talk about new models such as Prophet and tRNNs and tLSTMs.
After this, we do a hands-on lab session, where we practice all of the learned concepts in Python, using real world datasets. The training includes theory and hands-on exercises. After this training you will have gained knowledge about:
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