Time Series

Time Series Components you own this product

This free project is part of the liveProject series Time Series for Stock Price Prediction
intermediate Python • basic time series analysis • beginner machine learning • basics of Matplotlib • basics of statsmodel
skills learned
use Alpha Vantage API • explore data using ACF, PACF, and ADF tests • deal with non-stationary data
Abdullah Karasan
1 week · 4-6 hours per week · ADVANCED
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Imagine you’re a consultant with a number of prestigious financial institutions on your client list. One of them has hired you to increase the accuracy of their model for predicting stock prices. You rise to the challenge, deciding on some classical time series models. But before you can propose a reliable model, you must decompose and examine the time series data in order to understand its pattern. Once you have a firm grasp on the data’s peculiarities, you’ll be ready to run the time series modeling.

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 beginner-level deep learning knowledge, and be familiar with the following:

  • Intermediate Python
  • Basics of Matplotlib
  • Alpha Vantage API
  • Jupyter Notebook
  • Basics of statsmodel

you will learn

In this liveProject, you’ll gain experience exploring data in time series using visualization and statistical tools. You’ll learn to identify time series components and stationarity to understand patterns in your data and to tune your parameters as necessary to prepare for modeling.

  • Use Alpha Vantage API
  • Explore data using ACF, PACF, and ADF tests
  • Deal with non-stationary data


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