Fundamental Statistical and Machine Learning Models for Time Series Analysis

Abstract: 

This tutorial workshop will cover both statistical and neural network-based models for time series analysis. It will be introductory in nature and focus on the discussion of a couple of workhorse statistical and neural network-based time series models that are frequently applied to solving time series forecasting problems.

Specifically, I will sketch the family Autoregressive Integrated Moving Average (ARIMA) models (with and without seasonal components), the class of Vector Autoregressive (VAR) Models, and Long-Short Term Memory (LSTM) Network, including a discussion of the advantages and disadvantages when using each of these models in time series forecasting scenarios. I will use some real-world time series to illustrate the application of these techniques in Python.

Forecasting is both a fascinating subject to study and an important technique applied in industry, government and academic settings. Example applications include demand and inventory planning, marketing strategy planning, capital budgeting, pricing, machine predictive maintenance, macroeconomic forecasting, and supply chain forecasting.

Forecasting typically requires time series data, and time series data is ubiquitous nowadays, both within and outside of the data science field: weekly initial unemployment claims, tick-level stock prices, weekly company sales, daily number of steps taken recorded by a wearable, machine performance measurements recorded by sensors, key performance indicators of business functions, just to name a few.

However, time series data differs from cross-sectional data in that time series data has temporal dependence, and this dependence can be leveraged to forecast future values of the series. Some of the most important and commonly used data science techniques to analyze time series data and make forecasts are those developed in the field of statistics and machine learning. For this reason, time series statistical and machine learning models should be included in any data scientists’ toolkit.

Bio: 

Jeffrey Yau is currently Chief Data & A.I. Officer at Fanatics Collectibles. Most recently, he served as Global Head of Data Science, Analytics & Engineering at Amazon Music where he oversaw multiple teams who developed both insights-packed analytics and end-to-end statistical and machine learning systems. Prior to Amazon, Jeffrey worked at WalmartLabs as the VP of Data Science & Engineering where he led the team responsible for powering Walmart store mobile apps and the entire store finance system. Further, his team created end-to-end machine learning systems for key business initiatives and had a multi-billion dollar impact annually on Walmart U.S.

Over the years, he has held various senior level positions in quantitative finance at global investment management firm AllianceBernstein, consulting firm Data Science at Silicon Valley Data Science, multinational financial services company Charles Schwab Corporation, and the world's leading professional services firm KPMG. He began his career as a tenure-track Assistant Professor of Economics at Virginia Tech, and he was an adjunct professor at UC Berkeley, Cornell, and NYU, teaching machine learning and advanced statistical modeling for finance and business.

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