Traditional Analysis you own this product

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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Look inside

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.

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

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