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  3. Machine Learning for Time Series Data in Python
  • 1

    Time Series and Machine Learning Primer

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

  • 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.

  • 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.

  • 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.

Initializing

Ubung

Special case: Auto-regressive models

Now that you've created time-shifted versions of a single time series, you can fit an auto-regressive model. This is a regression model where the input features are time-shifted versions of the output time series data. You are using previous values of a timeseries to predict current values of the same timeseries (thus, it is auto-regressive).

By investigating the coefficients of this model, you can explore any repetitive patterns that exist in a timeseries, and get an idea for how far in the past a data point is predictive of the future.

Anweisungen

100 XP
  • Replace missing values in prices_perc_shifted with the median of the DataFrame and assign it to X.
  • Replace missing values in prices_perc with the median of the series and assign it to y.
  • Fit a regression model using the X and y arrays.