sunnydigital/ptsafall2022

Materials for NYU class DS-GA 1018.001 Probabilistic time series analysis

pTSAfall2022

Materials for NYU class DS-GA 1018.001 Probabilistic time series analysis

Lecture

Thursdays from 2:00pm-3:40pm SILV_206

Lab (required for all students)

DS-GA 1018.002 Lab (cap = 40) Tuesdays from 10:15am-11:05am 60FA_150

DS-GA 1018.003 Lab (cap = 40) Tuesdays from 11:15am-12:05pm KIMM_803

Instructor

Cristina Savin, [email protected] Office hours: TBD

TAs

Section 002 - Richard-John Lin ([email protected])

Section 003 - Jeroen Olieslagers ([email protected])

Overview

This graduate level course presents fundamental tools for characterizing data with statistical dependencies over time, and using this knowledge for predicting future outcomes. These methods have broad applications from econometrics to neuroscience.The course emphasizes generative models for time series, and inference and learning in such models. We will cover range of approaches including Kalman Filter, HMMs, AR(I)MA, Gaussian Processes, and their application to several kinds of data.

We refer you to the Brightspace syllabus for details.

Bibliography

There is no required textbook. Assigned readings will come from freely-available online material.

Core materials

• Time series analysis and its applications, by Shumway and Stoffer, 4th edition
• Pattern recognition and machine learning, Bishop
• Gaussian processes Rassmussen & Williams

Useful extras

• Review notes from Stanford's machine learning class
• Sam Roweis's probability review
• Carlos Ferndandez's notes on Statistics and Probability for Data Science DS-GA 1002

Academic honesty

We expect you to try solving each problem set on your own. However, if stuck you should discuss things with other students in the class, subject to the following rules:

• Brainstorming and verbally discussing the problem with other colleagues ok, going together through possible solutions, but should not involve one student telling another a complete solution.
• Once you solve the homework, you must write up your solutions on your own.
• You must write down the names of any person with whom you discussed it. This will not affect your grade.
• Do not consult other people's solutions from similar courses.
• Credit should be explicitly given for any code you use that you did not write yourself.
• Violations result in a zero score on that assignment, and a notice to the DGS.