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Event Registration - Introduction To Time Series Analysis In Python

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Date:
Friday, May 17, 2019
Time:
8:00 AM - 5:00 PM Eastern Daylight Time
Introduction to Time Series Analysis in Python

Overview
This hands-on data science course teaches the fundamentals of time series analysis and how to build time series models in Python. Whether you are trying to predict asset prices or understand the effects of air pollution over time, effective time series analysis can help you. At the end of the workshop, participants will be comfortable applying the Python programming language to visualize and execute time series analysis to see if there is predictive power in your data.

What This Course Offers
  • An overview of time series models and how to use them to solve real-world problems
  • Hands-on Python programming experience
  • Course notes, certificate of completion, and post-seminar email support for 3 months
  • An engaging and practical training approach with a qualified instructor with relevant
  • technical, business, and educational experiences
  • A Computer Science 101 pre-course webinar

Who Is This For
This course is relevant for individuals working with or needing to understand times series. The most common participants are: investment professionals, traders, economists, biologists, chemists, physicists, entrepreneurs, consultants, and technology individuals. Cognitir’s Introduction to Data Science course or the equivalent is required.

Course and Contact Information
Course Prerequisites: Introduction to Data Science is a prerequisite. If you have not been able to take this course with us yet, please contact us.
info@cognitir.com
+1 908 505 5991 (US); +44 75 0686 49 85 (UK)
www.cognitir.com

Course Curriculum
  • Overview of Time Series Analysis
    • What is it, wide variety of use cases, time series analysis vs. time series forecasting, common statistical problems in time series (leptokurtic, heteroskedasticity, serial correlation) and common tests to test for these issues (look at error residuals)
  • Organizing and Visualizing Time Series Data
    • Exploring Your Time Series Data
      • Start, end, frequency, number of data points
    • Basic Time Series Plots
    • Sampling Frequency
    • Missing Values
    • How to do this in Python • with an example
    • Organizing and Visualizing Time Series Coding Challenge
  • Time Series Stationarity
    • Trends
    • Random or Not
    • Stationary vs. Non-Stationary
      • Unit/root test
    • Removing variability trends through logarithmic transformation
    • Differencing
    • White Noise Model
    • Random Walk Model
    • How to do this in Python • with example
    • Time Series Stationarity Coding Challenge
  • Autocorrelation and Partial Autocorrelation
    • Financial Time Series
    • Autocorrelation and Calculation
    • Autocorrelation Function
    • Partial Autocorrelation Function
    • How to do this in Python
    • Autocorrelation and Partial Autocorrelation Coding Challenge
  • Time Series ARIMA Models
    • Autocorrelation and Autoregression
    • Random Walk vs. AR
    • Autocorrelation and simple moving averages
    • Selecting ARIMA model parameters
    • ARIMA model Estimate and Forecasting
    • How to do this in Python
    • ARIMA Model Coding Challenge
  • Time Series Model Evaluation
    • Visualizing model predictions
    • In Sample versus Out of Sample Accuracy
    • Types of time series error metrics
    • Model residual diagnostics
    • Model Evaluation Coding Challenge
  • Final Project
Address:
MCLE New England
4751, 10 Winter Pl
Boston, MA 02108
Description:Members: $399 Non- Members: $499
CE CreditCFA Boston has determined that this event qualifies for 8 CE credit hours under the guidelines of CFA Institute's Continuing Education Program. If you are a CFA Institute member, CE credit for your participation in this event will be automatically recorded in your CE tracking tool.
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