Time series data is ubiquitous. Whether it be stock market fluctuations, sensor data recording climate change, or activity in the brain, any signal that changes over time can be described as a time series. Machine learning has emerged as a powerful method for leveraging complexity in data in order to generate predictions and insights into the problem one is trying to solve. This course is an intersection between these two worlds of machine learning and time series data, and covers feature engineering, spectograms, and other advanced techniques in order to classify heartbeat sounds and predict stock prices.
Time Series and Machine Learning PrimerFree
This chapter is an introduction to the basics of machine learning, time series data, and the intersection between the two.
Time Series as Inputs to a Model
The easiest way to incorporate time series into your machine learning pipeline is to use them as features in a model. This chapter covers common features that are extracted from time series in order to do machine learning.
Predicting Time Series Data
If you want to predict patterns from data over time, there are special considerations to take in how you choose and construct your model. This chapter covers how to gain insights into the data before fitting your model, as well as best-practices in using predictive modeling for time series data.
Validating and Inspecting Time Series Models
Once you've got a model for predicting time series data, you need to decide if it's a good or a bad model. This chapter coves the basics of generating predictions with models in order to validate them against "test" data.
Fellow at the Berkeley Institute for Data Science
Chris Holdgraf is a fellow at the Berkeley Institute for Data Science at UC Berkeley. He has a PhD in cognitive neuroscience from UC Berkeley. His work is at the boundary between technology, open-source software, and scientific workflows. He's a core member of Project Jupyter and contributes to several other open source tools for data analytics and education.