Time Series Forecasting using Deep Learning

Combining PyTorch, RNN, TCN, and Deep Neural Network Models to Provide Production-Ready Prediction Solutions

Ivan Gridin

• Publisher: BPB Publications
• Year: 2021
• eBook ISBN: 9789391392574
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

Explore the infinite possibilities offered by Artificial Intelligence and Neural Networks

Key Features
? Covers numerous concepts, techniques, best practices and troubleshooting tips by community experts.
? Includes practical demonstration of robust deep learning prediction models with exciting use-cases.
? Covers the use of the most powerful research toolkit such as Python, PyTorch, and Neural Network Intelligence.

Description
This book is amid at teaching the readers how to apply the deep learning techniques to the time series forecasting challenges and how to build prediction models using PyTorch.The readers will learn the fundamentals of PyTorch in the early stages of the book. Next, the time series forecasting is covered in greater depth after the programme has been developed. You will try to use machine learning to identify the patterns that can help us forecast the future results. It covers methodologies such as Recurrent Neural Network, Encoder-decoder model, and Temporal Convolutional Network, all of which are state-of-the-art neural network architectures. Furthermore, for good measure, we have also introduced the neural architecture search, which automates searching for an ideal neural network design for a certain task.Finally by the end of the book, readers would be able to solve complex real-world prediction issues by applying the models and strategies learnt throughout the course of the book. This book also offers another great way of mastering deep learning and its various techniques.

What you will learn
? Work with the Encoder-Decoder concept and Temporal Convolutional Network mechanics.
? Learn the basics of neural architecture search with Neural Network Intelligence.
? Combine standard statistical analysis methods with deep learning approaches.
? Automate the search for optimal predictive architecture.
? Design your custom neural network architecture for specific tasks.
? Apply predictive models to real-world problems of forecasting stock quotes, weather, and natural processes.

Who this book is for
This book is written for engineers, data scientists, and stock traders who want to build time series forecasting programs using deep learning. Possessing some familiarity of Python is sufficient, while a basic understanding of machine learning is desirable but not needed.

Table of Contents
1. Time Series Problems and Challenges
2. Deep Learning with PyTorch
3. Time Series as Deep Learning Problem
4. Recurrent Neural Networks
5. Advanced Forecasting Models
6. PyTorch Model Tuning with Neural Network Intelligence
7. Applying Deep Learning to Real-world Forecasting Problems
8. PyTorch Forecasting Package
9. What is Next?

CHAPTER 1

Time Series Problems and Challenges

Time series data are a very important source of information. People always tried to analyze time series data to understand the nature of events. Since ancient times, mankind has begun to wonder what lies in the essence of changes in moon cycles, weather, temperature, the river water level, harvest, and so on. And the essential way is to collect time series data of certain events and try to analyze them. Time series analysis gave a necessary tool for future prediction. These days, time series analysis is being used everywhere: from marketing and finance to education, healthcare, climate research, and robotics. There are many practical and theoretical approaches to time series forecasting: mathematics, statistics, random process theory, and so on. Artificial intelligence model-based forecasting has also become a popular research tool for the past decade. We start exploring how the latest advances in deep learning can be applied to time series forecasting.

Structure

In this chapter, we will discuss the following topics:

• Introduction to time series analysis and time series forecasting
• Time series characteristics
• Common time series problems
• Classical approaches
• Promise of Deep Learning
• Python for time series analysis

Objectives

In this chapter, we will make a short introduction to time series analysis. We will show some examples of time series problems and their importance. Also, we will examine the most famous classical methods for time series forecasting. And finally, we will get an explanation of the perspectives of the Deep Learning approach.

Introduction to time series analysis and time series forecasting

Time series is a sequence of observations that depends on time. Time is an essential feature in natural processes such as air temperature, a pulse of the heart, or stock price changes. Chronological order is an essential part of time series data that has to be present at the time of collecting the data. Time series analysis involves working with time-based data to make forecasts about the future. The period is measured in seconds, minutes, hours, days, months, years, or any other time unit.

Each time series dataset can be presented in tabular form, where the first column contains time data. Dataset is always sorted in chronological order. In the following table we show an example of a time series dataset:

London average temperature in 2020

Month	Average Temperature (Celsius)
January	6.7
February	6.4
March	6.8
April	10.3
May	12.6
June	15.1
July	15.7
August	17.2
September	14
October	10.5
November	8.7
December	6

Table 1.1: London average temperature monthly

Of course, formal mathematical models work only with data, but it is easier for humans to work with a visual representation of time series data, take a look in Figure 1.1:

Figure 1.1: London average temperature monthly

Although we rely on complex mathematical models for solving problems, we should not underestimate the human ability to find patterns and dependencies. Therefore, the visual representation of time series data is an important part of the analysis. We'll come back to this topic later.

There is some difference between time series analysis and time series forecasting. These two fields are tightly correlated, but they serve slightly different tasks.

Time series analysis

Time series analysis recognizes the essence of time series data structure and extracts helpful information from time series: trend, cyclic and seasonal deviations, correlations, and so on.

Time series analysis solves the following tasks:

• Pre-process and perform feature extraction to get a meaningful and valid time series dataset.
• Obtain definite insights into the historical time series dataset.
• Data representation and visualization (graphical analysis, chart construction, report building)

Time series forecasting

Time series forecasting includes:

• Developing models.
• Using them to forecast future predictions.

Time series analysis is the first step to prepare and analyze time series dataset for time series forecasting.

Note: In this book, we will not stick to a strict definition of the term "time series analysis". We will also mean that the term time series analysis includes the problem of time series forecasting.

Time series characteristics

We have to understand how time series differ from one another. There are different types and classes of time series. Event short glance on time series graph can explain that the two processes that generated these datasets have a completely different nature.

Let us examine t...

Table of contents

• Cover Page
• Title Page
• Copyright Page
• About the Author
• About the Reviewer
• Acknowledgement
• Preface
• Errata
• Table of Contents
• 1. Time Series Problems and Challenges
• 2. Deep Learning with PyTorch
• 3. Time Series as Deep Learning Problem
• 4. Recurrent Neural Networks
• 5. Advanced Forecasting Models
• 6. PyTorch Model Tuning with Neural Network Intelligence
• 7. Applying Deep Learning to Real-world Forecasting Problems
• 8. PyTorch Forecasting Package
• 9. What is Next?
• Index