Presentation: Forecasting with Prophet
Share this on:
Abstract
Forecasting is a common data science task that helps organizations with capacity planning, goal setting, and anomaly detection. Despite its importance, there are serious challenges associated with producing reliable and high quality forecasts – especially when there are a variety of time series and analysts with expertise in time series modeling are relatively rare. To address these challenges, we describe a practical, modular approach to forecasting “at scale” based on a flexible curve fitting procedure that produces high quality forecasts across a wide variety of business time series.
2019 Tracks
-
Solving Software Engineering Problems with Machine Learning
Interesting machine learning use cases changing how we develop software today, including planned topics touching on infrastructure optimization, developer experience, security, and more.
-
Predictive Architectures in the Real World
Case Study focused look at end to end predictive pipelines from places like Salesforce, Uber, Linkedin, & Netflix.
-
Sequential Data: Natural Language, Time Series, and Sound
Techniques, practices, and approaches around time series and sequential data. Expect topics including image recognition, NLP/NLU, preprocess, & crunching of related algorithms.
-
Deep Learning in Practice
Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.
-
AI Meets the Physical World
Where AI touches the physical world, think drones, ROS, NVidia, TPU and more.
-
Papers in Production: Modern CS in the Real World
Groundbreaking papers make real-world impact.