Time series forecasting using deep learning with PyTorch
Who is this presentation for?
- Data scientists; statisticians; data science vice presidents, directors, and managers; and anyone who needs to conduct time series forecasting
Forecasting is both a fascinating subject to study and an important techniques 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 inside and outside of the data science field: weekly initial unemployment claims, tick-level stock prices, weekly company sales, the daily number of steps taken recorded by a wearable, machine performance measurements recorded by sensors, and 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 based on them 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.
Jeffrey Yau details the application of neural network-based techniques to time series forecasting problems and compares them to time series statistical models when modeling and forecasting time series with trends, multiple seasonality, regime switch, and exogenous series. Specifically, he explores how neural network-based models can model and forecast jointly multiple forecasting time series that may have trends, multiple seasonality, and potential regime switch, and how they can incorporate exogenous time series. He compares the characteristics of neural network-based techniques to the popular statistical models such as the class of vector autoregressive (VAR) models and regime switching models.
You’ll leave with an understanding of the advantages and disadvantages of using each of these models in time series forecasting scenarios. Real-world applications, such as dynamic asset allocation, are used to illustrate these techniques. Jeffrey also touches on exploratory time series data analysis.
- A working knowledge of Python
- A good understanding of classical linear regression models, basic machine learning techniques (support vector machine (SVM), tree-based models, and neural network-based models)
- General knowledge of time series forecasting
What you'll learn
- Learn the mathematical formulation and coding implementation of neural network-based models using PyTorch and the mathematical formulation, key characteristics, and coding implementation of VAR models framed using the state space formulation and regime switching models
- Understand the pros and cons of the application of neural network-based models, VAR, and regime switching models when modeling and forecasting time series with trends, multiple seasonality, and regime switch
Jeffrey Yau is a distinguished data scientist at WalmartLabs, where he leads data science for the store technology department. Previously, he was the chief data scientist at AllianceBernstein, a global asset-management firm; the vice president and head of data science at Silicon Valley Data Science, where he led a team of PhD data scientists helping companies transform their businesses using advanced data science techniques and emerging technology; the head of risk analytics at Charles Schwab; director of financial risk management consulting at KPMG; assistant director at Moody’s Analytics; and assistant professor of economics at Virginia Tech. Jeffrey’s active in the data science community and often speaks at data science conferences and local events. He has many years of experience in applying a wide range of econometric and machine learning techniques to create analytic solutions for financial institutions, businesses, and policy institutions. Jeffrey holds a PhD and an MA in economics from the University of Pennsylvania and a BS in mathematics and economics from UCLA.
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