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Time Series Forecasting (Python) Training

Time Series Forecasting (Python) Training

Due to the COVID-19 our training courses will be taught via an online classroom.

Receive in-depth knowledge from industry professionals, test your skills with hands-on assignments & demos, and get access to valuable resources and tools.

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.

Are you interested? Contact us and we will get in touch with you.

 

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Academy: Forecasting with Python
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About the training & classes

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:

  • Stationarity and statistical tests for it
  • STL decomposition
  • Time series transformations and differencing
  • Autocorrelation (ACF) and Partial Autocorrelation function (PACF)
  • ETS model
  • ARIMA model
  • STL-ARIMA model
  • In-sample evaluation methods: AIC, AICc and BIC
  • Diagnostics of the model residual
  • Out-of-sample evaluation metrics: MAE, RMSE, MAPE, MASE
  • Time series cross-validation
  • Prophet
  • tRNNs and tLSTMs