课程

Machine Learning for Time Series Data in Python

高级技能水平

更新时间 2026年2月

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

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PythonMachine Learning4 小时13 视频53 练习4,550 经验值52,775成就声明

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课程描述

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.

先决条件

Manipulating Time Series Data in Python Visualizing Time Series Data in Python Supervised Learning with scikit-learn

1

Time Series and Machine Learning Primer

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

Timeseries kinds and applications

50 经验值

Identifying a time series

50 经验值

Plotting a time series (I)

100 经验值

Plotting a time series (II)

100 经验值

Machine learning basics

50 经验值

Fitting a simple model: classification

100 经验值

Predicting using a classification model

100 经验值

Fitting a simple model: regression

100 经验值

Predicting using a regression model

100 经验值

Machine learning and time series data

50 经验值

Inspecting the classification data

100 经验值

Inspecting the regression data

100 经验值

2

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.

Classifying a time series

50 经验值

Many repetitions of sounds

100 经验值

Invariance in time

100 经验值

Build a classification model

100 经验值

Improving features for classification

50 经验值

Calculating the envelope of sound

100 经验值

Calculating features from the envelope

100 经验值

Derivative features: The tempogram

100 经验值

The spectrogram

50 经验值

Spectrograms of heartbeat audio

100 经验值

Engineering spectral features

100 经验值

Combining many features in a classifier

100 经验值

3

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.

Predicting data over time

50 经验值

Introducing the dataset

100 经验值

Fitting a simple regression model

100 经验值

Visualizing predicted values

100 经验值

Advanced time series prediction

50 经验值

Visualizing messy data

100 经验值

Imputing missing values

100 经验值

Transforming raw data

100 经验值

Handling outliers

100 经验值

Creating features over time

50 经验值

Engineering multiple rolling features at once

100 经验值

Percentiles and partial functions

100 经验值

Using "date" information

100 经验值

4

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.

Creating features from the past

50 经验值

Creating time-shifted features

100 经验值

Special case: Auto-regressive models

100 经验值

Visualize regression coefficients

100 经验值

Auto-regression with a smoother time series

100 经验值

Cross-validating time series data

50 经验值

Cross-validation with shuffling

100 经验值

Cross-validation without shuffling

100 经验值

Time-based cross-validation

100 经验值

Stationarity and stability

50 经验值

Stationarity

50 经验值

Bootstrapping a confidence interval

100 经验值

Calculating variability in model coefficients

100 经验值

Visualizing model score variability over time

100 经验值

Accounting for non-stationarity

100 经验值

50 经验值

Machine Learning for Time Series Data in Python

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