This chapter is an introduction to the basics of machine learning, time series data, and the intersection between the two.

Timeseries kinds and applications

Identifying a time series

Plotting a time series (I)

Plotting a time series (II)

Machine learning basics

Fitting a simple model: classification

Predicting using a classification model

Fitting a simple model: regression

Predicting using a regression model

Machine learning and time series data

Inspecting the classification data

Inspecting the regression data

Time Series and Machine Learning Primer

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.

Classifying a time series

Many repetitions of sounds

Invariance in time

Build a classification model

Improving features for classification

Calculating the envelope of sound

Calculating features from the envelope

Derivative features: The tempogram

The spectrogram

Spectrograms of heartbeat audio

Engineering spectral features

Combining many features in a classifier

Time Series as Inputs to a Model

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.

Predicting data over time

Introducing the dataset

Fitting a simple regression model

Visualizing predicted values

Advanced time series prediction

Visualizing messy data

Imputing missing values

Transforming raw data

Handling outliers

Creating features over time

Engineering multiple rolling features at once

Percentiles and partial functions

Using "date" information

Predicting Time Series Data

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.

Creating features from the past

Creating time-shifted features

Special case: Auto-regressive models

Visualize regression coefficients

Auto-regression with a smoother time series

Cross-validating time series data

Cross-validation with shuffling

Cross-validation without shuffling

Time-based cross-validation

Stationarity and stability

Stationarity

Bootstrapping a confidence interval

Calculating variability in model coefficients

Visualizing model score variability over time

Accounting for non-stationarity

Wrap-up

Validating and Inspecting Time Series Models

Prices

Audio

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.

Manipulating Time Series Data in Python

Visualizing Time Series Data in Python

Supervised Learning with scikit-learn

This course focuses on feature engineering and machine learning for time series data. 

Machine Learning for Time Series Data in Python

Machine Learning Scientist in Python

Time Series in Python

Stationarity

Machine Learning for Time Series Data in Python

Hands-on interactive exercise