Home Machine Learning Machine Learning Education Introduction to Time Series Forecasting

Introduction to Time Series Forecasting

Audio version of the article

Time series forecasting is an important area of machine learning that is often neglected.

It is important because there are so many prediction problems that involve a time component. These problems are neglected because it is this time component that makes time series problems more difficult to handle.

In this post, you will discover time series forecasting.

After reading this post, you will know:

  • Standard definitions of time series, time series analysis, and time series forecasting.
  • The important components to consider in time series data.
  • Examples of time series to make your understanding concrete.

Let’s get started.

Time Series

A normal machine learning dataset is a collection of observations.

For example:

observation #1
observation #2
observation #3

Time does play a role in normal machine learning datasets.

Time does play a role in normal machine learning datasets.

Predictions are made for new data when the actual outcome may not be known until some future date. The future is being predicted, but all prior observations are almost always treated equally. Perhaps with some very minor temporal dynamics to overcome the idea of “concept drift” such as only using the last year of observations rather than all data available.

A time series dataset is different.

Time series adds an explicit order dependence between observations: a time dimension.

This additional dimension is both a constraint and a structure that provides a source of additional information.

A time series is a sequence of observations taken sequentially in time.

— Page 1, Time Series Analysis: Forecasting and Control.

Time #1, observation
Time #2, observation
Time #3, observation

For example:

- Advertisment -

Most Popular

- Advertisment -