Time Series Forecasting Using Deep Learning

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Time Series Forecasting Using Deep Learning

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Author : Ivan Gridin
language : en
Publisher: BPB Publications
Release Date : 2021-10-15

Time Series Forecasting Using Deep Learning written by Ivan Gridin and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-15 with Computers categories.

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?

Modern Time Series Forecasting With Python

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Author : Manu Joseph
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-11-24

Modern Time Series Forecasting With Python written by Manu Joseph and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-24 with Computers categories.

Build real-world time series forecasting systems which scale to millions of time series by applying modern machine learning and deep learning concepts Key Features Explore industry-tested machine learning techniques used to forecast millions of time series Get started with the revolutionary paradigm of global forecasting models Get to grips with new concepts by applying them to real-world datasets of energy forecasting Book DescriptionWe live in a serendipitous era where the explosion in the quantum of data collected and a renewed interest in data-driven techniques such as machine learning (ML), has changed the landscape of analytics, and with it, time series forecasting. This book, filled with industry-tested tips and tricks, takes you beyond commonly used classical statistical methods such as ARIMA and introduces to you the latest techniques from the world of ML. This is a comprehensive guide to analyzing, visualizing, and creating state-of-the-art forecasting systems, complete with common topics such as ML and deep learning (DL) as well as rarely touched-upon topics such as global forecasting models, cross-validation strategies, and forecast metrics. You’ll begin by exploring the basics of data handling, data visualization, and classical statistical methods before moving on to ML and DL models for time series forecasting. This book takes you on a hands-on journey in which you’ll develop state-of-the-art ML (linear regression to gradient-boosted trees) and DL (feed-forward neural networks, LSTMs, and transformers) models on a real-world dataset along with exploring practical topics such as interpretability. By the end of this book, you’ll be able to build world-class time series forecasting systems and tackle problems in the real world.What you will learn Find out how to manipulate and visualize time series data like a pro Set strong baselines with popular models such as ARIMA Discover how time series forecasting can be cast as regression Engineer features for machine learning models for forecasting Explore the exciting world of ensembling and stacking models Get to grips with the global forecasting paradigm Understand and apply state-of-the-art DL models such as N-BEATS and Autoformer Explore multi-step forecasting and cross-validation strategies Who this book is for The book is for data scientists, data analysts, machine learning engineers, and Python developers who want to build industry-ready time series models. Since the book explains most concepts from the ground up, basic proficiency in Python is all you need. Prior understanding of machine learning or forecasting will help speed up your learning. For experienced machine learning and forecasting practitioners, this book has a lot to offer in terms of advanced techniques and traversing the latest research frontiers in time series forecasting.

Deep Learning For Time Series Forecasting

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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2018-08-30

Deep Learning For Time Series Forecasting written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-30 with Computers categories.

Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality. With clear explanations, standard Python libraries, and step-by-step tutorial lessons you’ll discover how to develop deep learning models for your own time series forecasting projects.

Deep Time Series Forecasting With Python

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Author : N. Lewis
language : en
Publisher:
Release Date : 2016-12-11

Deep Time Series Forecasting With Python written by N. Lewis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-11 with categories.

Master Deep Time Series Forecasting with Python! Deep Time Series Forecasting with Python takes you on a gentle, fun and unhurried practical journey to creating deep neural network models for time series forecasting with Python. It uses plain language rather than mathematics; And is designed for working professionals, office workers, economists, business analysts and computer users who want to try deep learning on their own time series data using Python. QUICK AND EASY: Using plain language, this book offers a simple, intuitive, practical, non-mathematical, easy to follow guide to the most successful ideas, outstanding techniques and usable solutions available using Python. Examples are clearly described and can be typed directly into Python as printed on the page. NO EXPERIENCE? I'm assuming you never did like linear algebra, don't want to see things derived, dislike complicated computer code, and you're here because you want to see how to use deep learning for time series forecasting explained in plain language, and try it out for yourself. THIS BOOK IS FOR YOU IF YOU WANT: Explanations rather than mathematical derivation Real world applications that make sense. Illustrations to deepen your understanding. Worked examples you can easily follow and immediately implement. Ideas you can actually use and try on your own data. CUT LEARNING TIME IN HALF!: This guide was written for people who want to get up to speed as soon as possible. Through a simple to follow process you will learn how to build deep time series forecasting models in the minimum amount of time using Python. Once you have mastered the process, it will be easy for you to translate your knowledge into your own powerful business applications. YOU'LL LEARN HOW TO: Unleash the power of Long Short-Term Memory Neural Networks . Develop hands on skills using the Gated Recurrent Unit Neural Network. Design successful applications with Recurrent Neural Networks. Deploy Nonlinear Auto-regressive Network with Exogenous Inputs.. Adapt Deep Neural Networks for Time Series Forecasting. Master strategies to build superior Time Series Models. Everything you need to get started is contained within this book. Deep Time series Forecasting with Python is your very own hands on practical, tactical, easy to follow guide to mastery. Buy this book today and accelerate your progress!

Time Series Forecasting Using Generative Ai

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Author : Vishwas B V
language : en
Publisher: Apress
Release Date : 2025-04-07

Time Series Forecasting Using Generative Ai written by Vishwas B V and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-07 with Computers categories.

"Time Series Forecasting Using Generative AI introduces readers to Generative Artificial Intelligence (Gen AI) in time series analysis, offering an essential exploration of cutting-edge forecasting methodologies." The book covers a wide range of topics, starting with an overview of Generative AI, where readers gain insights into the history and fundamentals of Gen AI with a brief introduction to large language models. The subsequent chapter explains practical applications, guiding readers through the implementation of diverse neural network architectures for time series analysis such as Multi-Layer Perceptrons (MLP), WaveNet, Temporal Convolutional Network (TCN), Bidirectional Temporal Convolutional Network (BiTCN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Deep AutoRegressive(DeepAR), and Neural Basis Expansion Analysis(NBEATS) using modern tools. Building on this foundation, the book introduces the power of Transformer architecture, exploring its variants such as Vanilla Transformers, Inverted Transformer (iTransformer), DLinear, NLinear, and Patch Time Series Transformer (PatchTST). Finally, The book delves into foundation models such as Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM enabling readers to implement sophisticated forecasting models tailored to their specific needs. This book empowers readers with the knowledge and skills needed to leverage Gen AI for accurate and efficient time series forecasting. By providing a detailed exploration of advanced forecasting models and methodologies, this book enables practitioners to make informed decisions and drive business growth through data-driven insights. Understand the core concepts, history, and applications of Gen AI and its potential to revolutionize time series forecasting. Learn to implement different neural network architectures such as MLP, WaveNet, RNN, LSTM, DeepAR, and NBEATS for time series forecasting. Discover the potential of Transformer architecture and its variants, such as Vanilla Transformers, iTransformer, and PatchTST, for time series forecasting. Explore complex foundation models like Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM. How to apply Gen AI techniques to real-world time series forecasting challenges and make data-driven decisions. Who this book is for: Data Scientists, Machine learning engineers, Business Aanalysts, Statisticians, Economists, Financial Analysts, Operations Research Analysts, Data Analysts, Students.

Deep Learning With Pytorch Lightning

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Author : Kunal Sawarkar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-04-29

Deep Learning With Pytorch Lightning written by Kunal Sawarkar and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-29 with Computers categories.

Build, train, deploy, and scale deep learning models quickly and accurately, improving your productivity using the lightweight PyTorch Wrapper Key FeaturesBecome well-versed with PyTorch Lightning architecture and learn how it can be implemented in various industry domainsSpeed up your research using PyTorch Lightning by creating new loss functions, networks, and architecturesTrain and build new algorithms for massive data using distributed trainingBook Description PyTorch Lightning lets researchers build their own Deep Learning (DL) models without having to worry about the boilerplate. With the help of this book, you'll be able to maximize productivity for DL projects while ensuring full flexibility from model formulation through to implementation. You'll take a hands-on approach to implementing PyTorch Lightning models to get up to speed in no time. You'll start by learning how to configure PyTorch Lightning on a cloud platform, understand the architectural components, and explore how they are configured to build various industry solutions. Next, you'll build a network and application from scratch and see how you can expand it based on your specific needs, beyond what the framework can provide. The book also demonstrates how to implement out-of-box capabilities to build and train Self-Supervised Learning, semi-supervised learning, and time series models using PyTorch Lightning. As you advance, you'll discover how generative adversarial networks (GANs) work. Finally, you'll work with deployment-ready applications, focusing on faster performance and scaling, model scoring on massive volumes of data, and model debugging. By the end of this PyTorch book, you'll have developed the knowledge and skills necessary to build and deploy your own scalable DL applications using PyTorch Lightning. What you will learnCustomize models that are built for different datasets, model architectures, and optimizersUnderstand how a variety of Deep Learning models from image recognition and time series to GANs, semi-supervised and self-supervised models can be builtUse out-of-the-box model architectures and pre-trained models using transfer learningRun and tune DL models in a multi-GPU environment using mixed-mode precisionsExplore techniques for model scoring on massive workloadsDiscover troubleshooting techniques while debugging DL modelsWho this book is for This deep learning book is for citizen data scientists and expert data scientists transitioning from other frameworks to PyTorch Lightning. This book will also be useful for deep learning researchers who are just getting started with coding for deep learning models using PyTorch Lightning. Working knowledge of Python programming and an intermediate-level understanding of statistics and deep learning fundamentals is expected.

Time Series Forecasting In Python

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Author : Marco Peixeiro
language : en
Publisher: Simon and Schuster
Release Date : 2022-10-04

Time Series Forecasting In Python written by Marco Peixeiro and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-04 with Computers categories.

Build predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting. In Time Series Forecasting in Python you will learn how to: Recognize a time series forecasting problem and build a performant predictive model Create univariate forecasting models that account for seasonal effects and external variables Build multivariate forecasting models to predict many time series at once Leverage large datasets by using deep learning for forecasting time series Automate the forecasting process Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You’ll explore interesting real-world datasets like Google’s daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology You can predict the future—with a little help from Python, deep learning, and time series data! Time series forecasting is a technique for modeling time-centric data to identify upcoming events. New Python libraries and powerful deep learning tools make accurate time series forecasts easier than ever before. About the book Time Series Forecasting in Python teaches you how to get immediate, meaningful predictions from time-based data such as logs, customer analytics, and other event streams. In this accessible book, you’ll learn statistical and deep learning methods for time series forecasting, fully demonstrated with annotated Python code. Develop your skills with projects like predicting the future volume of drug prescriptions, and you’ll soon be ready to build your own accurate, insightful forecasts. What's inside Create models for seasonal effects and external variables Multivariate forecasting models to predict multiple time series Deep learning for large datasets Automate the forecasting process About the reader For data scientists familiar with Python and TensorFlow. About the author Marco Peixeiro is a seasoned data science instructor who has worked as a data scientist for one of Canada’s largest banks. Table of Contents PART 1 TIME WAITS FOR NO ONE 1 Understanding time series forecasting 2 A naive prediction of the future 3 Going on a random walk PART 2 FORECASTING WITH STATISTICAL MODELS 4 Modeling a moving average process 5 Modeling an autoregressive process 6 Modeling complex time series 7 Forecasting non-stationary time series 8 Accounting for seasonality 9 Adding external variables to our model 10 Forecasting multiple time series 11 Capstone: Forecasting the number of antidiabetic drug prescriptions in Australia PART 3 LARGE-SCALE FORECASTING WITH DEEP LEARNING 12 Introducing deep learning for time series forecasting 13 Data windowing and creating baselines for deep learning 14 Baby steps with deep learning 15 Remembering the past with LSTM 16 Filtering a time series with CNN 17 Using predictions to make more predictions 18 Capstone: Forecasting the electric power consumption of a household PART 4 AUTOMATING FORECASTING AT SCALE 19 Automating time series forecasting with Prophet 20 Capstone: Forecasting the monthly average retail price of steak in Canada 21 Going above and beyond

State Of The Art Deep Learning For Multi Product Intermittent Time Series Forecasting

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Author : Ronish Samir Raval
language : en
Publisher:
Release Date : 2021

State Of The Art Deep Learning For Multi Product Intermittent Time Series Forecasting written by Ronish Samir Raval and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.

Deep learning is gaining traction and considerable attention due to the state-of-the-art results obtained in computer vision, object detection, natural language processing, sequential analysis, and multiple other domains. Study of literature reveals that time series analysis is a good candidate for modeling using deep learning techniques. Time series analysis has applications from finance to supply chain domains and proves to be critical in driving organizations' profit and strategic growth. In a retail setting, product demand forecasting helps in minimizing inventory, optimizing service levels, and maximizing revenue. When dealing with demand forecasting, a much complex branch of intermittent demand profiles arises. When forecasting time series, the standard option comes down to statistical learning methods such as ARIMA, exponential smoothing, and several other models. However, in case of intermittency in demand and forecasting multiple time series at once, statistical learning methods fail to provide a high level of accuracy and can sometimes become computationally expensive as well. Deep learning algorithms enter the fray, as they can be applied to tackle the problem of forecasting intermittent sales while solving the problem in a computationally frugal manner. The study focuses on solving these two problems using a state-of-the-art based approach. It helps us answer the questions of -- How to implement neural networks in a value-add manner? And which models and architectures work best in our time series prediction problem with similar real-world applications? The study reveals that recurrent and convolutional architectures exhibit versatility and value in solving this problem, helping us understand the deep learning models and their application architectures in real-world scenarios. In this thesis, we have tried to answer these two important questions. The data was obtained from Kaggle for the M5 forecasting competition. The dataset relates to the daily Walmart sales of 3,000 products ranging across 10 stores. The data comprises of 3 different categories and 7 sub-categories, making it a multi-time series forecasting problem. We have applied the methods of statistical learning and deep learning to solve this problem. Statistical models of naïve method, moving average, ARIMA, Croston forecasting have been implemented. In deep learning, we initially use the deep feed-forward neural network to forecast the sales. Then recurrent architectures of RNN, LSTM and GRU are applied. Sequence learning and Attention mechanism have been implemented. Convolutional architectures of CNN, Wavenet, and temporal convolutional network have also been experimented for our problem. For the methodology, we initially select a single time series from the dataset and apply the statistical and deep learning models. This step in the methodology provides us with a strong fundamental understanding of how deep learning models are tuned to obtain the optimal architecture. Then, using the results from a single time series forecasting problem, we shortlist the most optimal deep learning models and their optimal architectures, to solve the problem of time series forecasting. We conclude that recurrent architectures provide the optimal solutions for our analysis (we define optimality through error minimization), and state-of-the-art models such as attention mechanism and sequence learning provide results within acceptable range, but their models are too computationally expensive to learn for multiple epochs and forecasts. We then conclude our analysis by providing important areas to focus on deep learning for time series forecasting in our future work.

Machine Learning For Time Series With Python

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Author : Ben Auffarth
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-10-29

Machine Learning For Time Series With Python written by Ben Auffarth and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-29 with Computers categories.

Get better insights from time-series data and become proficient in model performance analysis Key FeaturesExplore popular and modern machine learning methods including the latest online and deep learning algorithmsLearn to increase the accuracy of your predictions by matching the right model with the right problemMaster time series via real-world case studies on operations management, digital marketing, finance, and healthcareBook Description The Python time-series ecosystem is huge and often quite hard to get a good grasp on, especially for time-series since there are so many new libraries and new models. This book aims to deepen your understanding of time series by providing a comprehensive overview of popular Python time-series packages and help you build better predictive systems. Machine Learning for Time-Series with Python starts by re-introducing the basics of time series and then builds your understanding of traditional autoregressive models as well as modern non-parametric models. By observing practical examples and the theory behind them, you will become confident with loading time-series datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature engineering. This book will also guide you in matching the right model to the right problem by explaining the theory behind several useful models. You'll also have a look at real-world case studies covering weather, traffic, biking, and stock market data. By the end of this book, you should feel at home with effectively analyzing and applying machine learning methods to time-series. What you will learnUnderstand the main classes of time series and learn how to detect outliers and patternsChoose the right method to solve time-series problemsCharacterize seasonal and correlation patterns through autocorrelation and statistical techniquesGet to grips with time-series data visualizationUnderstand classical time-series models like ARMA and ARIMAImplement deep learning models, like Gaussian processes, transformers, and state-of-the-art machine learning modelsBecome familiar with many libraries like Prophet, XGboost, and TensorFlowWho this book is for This book is ideal for data analysts, data scientists, and Python developers who want instantly useful and practical recipes to implement today, and a comprehensive reference book for tomorrow. Basic knowledge of the Python Programming language is a must, while familiarity with statistics will help you get the most out of this book.

Future Data And Security Engineering Big Data Security And Privacy Smart City And Industry 4 0 Applications

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Author : Tran Khanh Dang
language : en
Publisher: Springer Nature
Release Date : 2021-11-13

Future Data And Security Engineering Big Data Security And Privacy Smart City And Industry 4 0 Applications written by Tran Khanh Dang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-13 with Computers categories.

This book constitutes the proceedings of the 8th International Conference on Future Data and Security Engineering, FDSE 2021, held in Ho Chi Minh City, Vietnam, in November 2021.* The 28 full papers and 8 short were carefully reviewed and selected from 168 submissions. The selected papers are organized into the following topical headings: big data analytics and distributed systems; security and privacy engineering; industry 4.0 and smart city: data analytics and security; blockchain and access control; data analytics and healthcare systems; and short papers: security and data engineering. * The conference was held virtually due to the COVID-19 pandemic.