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Deep Learning for Time Series Cookbook

You're reading from   Deep Learning for Time Series Cookbook Use PyTorch and Python recipes for forecasting, classification, and anomaly detection

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Product type Paperback
Published in Mar 2024
Publisher Packt
ISBN-13 9781805129233
Length 274 pages
Edition 1st Edition
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Authors (2):
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Luís Roque Luís Roque
Author Profile Icon Luís Roque
Luís Roque
Vitor Cerqueira Vitor Cerqueira
Author Profile Icon Vitor Cerqueira
Vitor Cerqueira
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Toc

Table of Contents (12) Chapters Close

Preface 1. Chapter 1: Getting Started with Time Series 2. Chapter 2: Getting Started with PyTorch FREE CHAPTER 3. Chapter 3: Univariate Time Series Forecasting 4. Chapter 4: Forecasting with PyTorch Lightning 5. Chapter 5: Global Forecasting Models 6. Chapter 6: Advanced Deep Learning Architectures for Time Series Forecasting 7. Chapter 7: Probabilistic Time Series Forecasting 8. Chapter 8: Deep Learning for Time Series Classification 9. Chapter 9: Deep Learning for Time Series Anomaly Detection 10. Index 11. Other Books You May Enjoy

Building simple forecasting models

Before diving into more complex methods, let’s get started with some simple forecasting models: the naive, seasonal naive, and mean models.

Getting ready

In this chapter, we focus on forecasting problems involving univariate time series. Let’s start by loading one of the datasets we explored in Chapter 1:

import pandas as pd
serie = pd.read_csv(
    "assets/datasets/time_series_solar.csv",
    parse_dates=["Datetime"],
    index_col="Datetime",
)['Incoming Solar']

In the preceding code, series is a pandas Series object that contains the univariate time series.

How to do it…

We can now forecast our time series using the three following methods:

  • Naive: The simplest forecasting method is the naive approach. This method assumes that the next observation is the same as the last one. In Python, it could be implemented as...
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