Real World Temporal Anomaly Detection through Supervised Machine Learning and Set Theory
Last Updated on August 18, 2023 by Editorial Team
Author(s): Ashutosh Malgaonkar
Originally published on Towards AI.

This member-only story is on us. Upgrade to access all of Medium.
Explore Open Data from the City of Seattle
Table Of Contents:
I. The Problem Statement
II. Remodeling time series into a supervised problem
III. Supervised Modeling and Analysis
I. Problem Statement
The data can be downloaded from here: Seattle Burke Gilman Trail U+007C Kaggle
The essence of this problem statement is that we need to detect anomalies 3 hours in advance. An anomaly is defined as >500 total people on the trail 3 hours from now. In order to solve this problem, we have been given per-hour data of trail traffic — pedestrian and bike.
II. Remodeling… Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Towards AI Academy Resources:
We build Enterprise AI. We teach what we learn. 15 AI Experts. 5 practical AI courses. 100k students
Free: 6-day Agentic AI Engineering Email Guide
Get your free Agents Cheatsheet here. Our proven framework for choosing the right AI architecture.
3 years of hands-on work with real clients into 6 pages.
Take our 90+ lesson From Beginner to Advanced LLM Developer Certification: From choosing a project to deploying a working product this is the most comprehensive and practical LLM course out there!
Discover Your Dream AI Career at Towards AI JobsOur jobs board is tailored specifically to AI, Machine Learning and Data Science Jobs and Skills. Explore over 100,000 live AI jobs today with Towards AI Jobs!
Note: Article content contains the views of the contributing authors and not Towards AI.