Time Series

Traditional Analysis you own this product

This project is part of the liveProject series Time Series for Stock Price Prediction
prerequisites
intermediate Python • time series analysis and stationarity analysis using statsmodel • basics of Matplotlib • intermediate machine learning
skills learned
model time series using moving average (simple MA and exponential MA), autoregressive (AR), and autoregressive moving average (ARIMA) models • visualize and compare performance using root mean square error (RMSE)
Abdullah Karasan
1 week · 4-6 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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Now that you’ve detected the time series components and obtained the stationary data, you’re ready to move on to time series modeling. In this liveProject, your challenge is to determine which model will perform best for your client’s data. To do this, you’ll apply the classical moving average (simple MA and exponential MA), autoregressive (AR), and autoregressive integrated moving average (ARIMA) models. Then you’ll compare their performance using a visualization and performance metric, root mean square error (RMSE).

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 the basics of time series analysis, have intermediate-level machine learning knowledge, and be familiar with the following:


TOOLS
  • Intermediate Python
  • Basics of Matplotlib
  • Jupyter Notebook
TECHNIQUES:
  • Classical time series analysis and stationarity analysis using statsmodel
  • Time series analysis related to tools such as ACF, PACF, and ADF test

you will learn

In this liveProject, you’ll learn to apply classical time series models and compare their performance in order to identify the best model for the application.


  • Time series modeling
  • Visualizing
  • Performance comparison

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
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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