Machine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods

Sampul Depan
Packt Publishing Ltd, 29 Okt 2021 - 370 halaman

Get better insights from time-series data and become proficient in model performance analysis

Key Features
  • Explore popular and modern machine learning methods including the latest online and deep learning algorithms
  • Learn to increase the accuracy of your predictions by matching the right model with the right problem
  • Master time series via real-world case studies on operations management, digital marketing, finance, and healthcare
Book DescriptionThe 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 learn
  • Understand the main classes of time series and learn how to detect outliers and patterns
  • Choose the right method to solve time-series problems
  • Characterize seasonal and correlation patterns through autocorrelation and statistical techniques
  • Get to grips with time-series data visualization
  • Understand classical time-series models like ARMA and ARIMA
  • Implement deep learning models, like Gaussian processes, transformers, and state-of-the-art machine learning models
  • Become familiar with many libraries like Prophet, XGboost, and TensorFlow
Who 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.

 

Halaman terpilih

Isi

Introduction to Time Series with Python
1
TimeSeries Analysis with Python
35
Preprocessing Time Series
67
Introduction to Machine Learning for TimeSeries
93
TimeSeries Forecasting with Moving Averages and Autoregressive Models
131
Unsupervised Methods for TimeSeries
161
Machine Learning Models for TimeSeries
185
Online Learning for TimeSeries
209
Probabilistic Models for TimeSeries
235
Deep Learning for TimeSeries
261
Reinforcement Learning for TimeSeries
297
Multivariate Forecasting
319
Packt Page
337
Other Books You May Enjoy
339
Index
345
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Machine Learning for Time-Series with Python: Forecast, Predict, and Detect ...
Ben Auffarth
Pratinjau tidak tersedia - 2021

Istilah dan frasa umum

anomaly detection Anthony Bagnall applied ARIMA autocorrelation Autoencoders bandits Box-Cox transformation causal change point chapter concept drift convolutional correlation data points DataFrame datetime deep learning deep learning models DeepAR discussed drift detection dynamic time warping ensemble environment error estimate example exponential smoothing Figure function fuzzy GitHub gradient boosting Here's HIVE-COTE implementations input introduced Jupyter notebook layer Learning for Time-Series learning methods libraries linear look LSTM machine learning algorithms machine learning model Markov mean metric Models for Time-Series moving average multivariate time-series neurons NumPy online learning outliers output parameters performance pip install plot predictions probabilistic models Prophet Python Q-learning random random forest recurrent neural networks regression reinforcement learning repository ResNet reward ROCKET sequence shapelets Silverkite sktime stationary statistics statsmodels supervised learning target techniques TensorFlow time-series analysis time-series classification time-series forecasting time-series models transformation trend and seasonality univariate unsupervised variables XGBoost

Tentang pengarang (2021)

Ben Auffarth is a full-stack data scientist with more than 15 years of work experience. With a background and Ph.D. in computational and cognitive neuroscience, he has designed and conducted wet lab experiments on cell cultures, analyzed experiments with terabytes of data, run brain models on IBM supercomputers with up to 64k cores, built production systems processing hundreds and thousands of transactions per day, and trained language models on a large corpus of text documents. He co-founded and is the former president of Data Science Speakers, London.

Informasi bibliografi

JudulMachine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods
PengarangBen Auffarth
PenerbitPackt Publishing Ltd, 2021
ISBN1801816107, 9781801816106
Tebal370 halaman
  
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