Practical Time-Series Forecast and Anomaly Detection in Python, Dr. Ahmed Abdulaal 20191028

Dr. Ahmed Abdulaal, Data Scientist, eBay
We will walk through tackling a real-world time-series problem with code in python. First, we shall briefly go over some of the different approaches to tackling general time-series problems from statistical, Bayesian, and machine learning viewpoints with example code. Then, we will discuss the nature of outliers (Anomalies) and the challenges to identifying them in real-world applications, such as e-commerce.

Depending on the discussion pace and as time permits, we will demonstrate the data-scientist process of feature engineering, model selection, tuning, ensembling, and evaluation for addressing the specific problem and its challenges.

Finally, we’ll go over model-productionalizing and setting up dashboards for results communication for the upcoming longer training class to finish. The objective is to provide a quick overview of the methods and tools for time-series modeling with practical code. This talk acts as a preparatory tutorial for a longer ACM seminar of diving deeper into the science and practice of time-series analysis to come.

For the example time-series problem, we’ll use an eCommerce data set with the objective of detecting anomalies such as service interruptions and incidents.

The talk assumes that the audience have basic knowledge of the Python programming language and data-handling libraries such as Pandas and Numpy, or equivalent libraries in other languages (R, Octave, Matlab, etc.).

Further preparation is not required. A list of reading material and other resources relevant to the discussion topic will be provided for upcoming longer ACM seminar.

Speaker Bio:
Ahmed Abdulaal had joined eBay’s Operations Analytics team as a Data Scientist in Spring 2018. Ahmed’s experience is in Optimization, Simulation, and Machine Learning with applications to Time-Series data. In Fall 2017, he received his Ph.D. in Industrial Engineering from the University of Miami, where he had worked with time-series energy data and contributed to research with 7 scientific publications covering topics like Building Energy Optimization, Electric Vehicle Routing, and Electricity Patterns Classification. Before graduation, Ahmed interned at the Walt Disney Company as a Decision Scientist, where he had worked on production-level time-series media forecast models, then he joined Comcast NBCUniversal’s Golf Channel Division as an Operations Research Scientist to work on demand forecast and price optimization models, as well as A/B testing. Ahmed received his B.S. in Mechanical Engineering from Cairo University, Egypt, his M..S. in Industrial Engineering, and his M.B.A. from the University of New Haven, Connecticut.

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