Book Image

Hands-On Ensemble Learning with R

By : Prabhanjan Narayanachar Tattar
Book Image

Hands-On Ensemble Learning with R

By: Prabhanjan Narayanachar Tattar

Overview of this book

Ensemble techniques are used for combining two or more similar or dissimilar machine learning algorithms to create a stronger model. Such a model delivers superior prediction power and can give your datasets a boost in accuracy. Hands-On Ensemble Learning with R begins with the important statistical resampling methods. You will then walk through the central trilogy of ensemble techniques – bagging, random forest, and boosting – then you'll learn how they can be used to provide greater accuracy on large datasets using popular R packages. You will learn how to combine model predictions using different machine learning algorithms to build ensemble models. In addition to this, you will explore how to improve the performance of your ensemble models. By the end of this book, you will have learned how machine learning algorithms can be combined to reduce common problems and build simple efficient ensemble models with the help of real-world examples.
Table of Contents (17 chapters)
Hands-On Ensemble Learning with R
What's Next?

Chapter 11. Ensembling Time Series Models

All of the models developed in this book so far have dealt with situations that arise when observations are independent of each other. The example of overseas visitors explains a time series in which the observations are dependent on the previously observed data. In a brief discussion of this example, it was established that it is necessary to develop time series models. Since the time series is sequential in nature, the time stamp may be displayed in nanoseconds, seconds, minutes, hours, days, or months.

This chapter will open with a quick review of the important concepts of time series in autocorrelation and partial autocorrelation functions, as well as fitted model assessment measures. Much like the classification and regression models, a host of methods are available for analyzing time series data. An important class of time series models in seasonal decomposition includes LOESS (STL), exponential smoothing state space models (ets), Box-Jenkins...