Time Series

Hybrid Model you own this product

This project is part of the liveProject series Time Series for Stock Price Prediction
intermediate Python • basics of Matplotlib • basics of Jupyter Notebook • intermediate machine learning • basics of TensorFlow
skills learned
combine classical and deep learning models • visualize results • evaluate model performance
Abdullah Karasan
1 week · 4-6 hours per week · BEGINNER

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Now that you’ve prepared the data for the deep learning models, you’re ready to apply the hybrid model, which leverages the strengths of both the classical and deep learning models. In this liveProject, you’ll focus on the essential steps for running a time series analysis. You’ll start with ensuring the data you prepared is ready for processing with the deep learning models. Next, you’ll run the RNN and LSTM for the separate datasets. Finally, you’ll evaluate the performance of your models.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

Abdullah Karasan
Abdullah Karasan was born in Berlin, Germany. After studying economics and business administration, he obtained his master's degree in applied economics from the University of Michigan, Ann Arbor, and his PhD in financial mathematics from the Middle East Technical University, Ankara. He is a former Treasury employee of Turkey and currently works as a principal data scientist at Magnimind and as a lecturer at the University of Maryland, Baltimore. He has also published several papers in the field of financial data science.


This liveProject is for finance practitioners and anyone interested in gaining hands-on experience with time series analysis in finance. To begin this liveProject you will need to know the basics of time series analysis, have intermediate machine learning knowledge, and be familiar with the following:

  • Intermediate Python knowledge
  • Basics of NumPy
  • Basics of Matplotlib
  • Basics of TensorFlow
  • Jupyter Notebook
  • Intermediate machine learning
  • Basics of time series analysis

you will learn

In this liveProject, you’ll focus on preparing data for deep learning models:

  • Hybrid approach for time series analysis: Combining the classical and deep learning techniques (ARIMA and LSTM)
  • Visualizing the results
  • Evaluating the performance


You choose the schedule and decide how much time to invest as you build your project.
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book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

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