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Time Series Analysis and Prediction - Financial and other Streaming Data

Instructor: Mike Bowles, PhD

Location: Hacker Dojo (also available as webcast and recording)

Dates: Oct 12, 26, Nov 2, 9

Time: 9:00 am till 1:00 pm

Registration: Early Bird registraion 5 days before class starts $295 (for all 4 classes) through or by PayPal (mike at mbowles dot com).  Regular registration $345 through eventbrite, PayPal or in class by check or cash.

This course will cover the basics of time series analysis and prediction.  In particular, we'll address topics used in conjunction with financial time series and topics currently being adapted to handle large volume streaming data.  The class will employ examples in R-code.  The main text for the course will be Analysis of Financial Time Series by Ruey S. Tsay.  Here's an outline.

Week        Topics

1st Week    Background for Time Series

Definitions, Stationarity, Tests, Examples

2nd Week  Linear Models

General Linear Model,  Fitting MA, ARMA, ARIMA etc.  ARCH and GARCH volatility models

3rd Week    Other Topics in Financial Time Series

Ito calculus and Black-Scholes model for option prices, Market microstructure, Co-integration and Pairs Trading, Basket trading


4th Week    Kalman Filters and Non-parametric models

Simple Derivation of Kalman Filter, General Form of Multivariate KF, Identifying KF State Space Model from Time Series Data,     Singular Spectrum Analysis and Change Point Detection

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