Time Series

Deep Learning you own this product

This project is part of the liveProject series Time Series for Stock Price Prediction
prerequisites
intermediate Python • basics of Matplotlib • basic time series analysis • basics of TensorFlow
skills learned
prepare data for deep learning models • run RNN and LSTM deep learning models • visualize results • evaluate model performance
Abdullah Karasan
1 week · 6-8 hours per week · ADVANCED
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liveProject This project is part of the liveProject series Time Series for Stock Price Prediction liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $19.99 $29.99 you save $10 (33%)
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You’ve shown your expertise in successfully modeling a time series using classical models: moving average (simple MA and exponential MA), autoregressive (AR), and autoregressive integrated moving average (ARIMA). Your next step is to determine if there’s a way to boost the performance of the time series analysis. You decide you’ll apply an unconventional approach that’s increasingly popular in finance circles: a hybrid model that combines deep learning and classical approaches. For the deep learning models, you choose recurrent neural network (RNN) and long short-term memory (LSTM). In this liveProject, you’ll focus on preparing the data for the deep learning models. The good news is that in deep learning models, you avoid a long and cumbersome preprocessing stage since, unlike in many classical approaches, they are able to detect patterns almost automatically.

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.

prerequisites

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 and be familiar with the following:


TOOLS:
  • Intermediate Python
  • Basics of Matplotlib
  • Basics of NumPy
  • Basics of Jupyter Notebook
  • Basics of TensorFlow
TECHNIQUES:
  • Basics of time series analysis
  • Basics of deep learning

you will learn

In this liveProject, you’ll learn to prepare data specifically for deep learning models:


  • Data preparation in deep learning
  • Deep learning models (RNN and LSTM)
  • Visualizing the results
  • Evaluating the performance

features

Self-paced
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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