Machine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods - Image 1

£39.99

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

Name: Machine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods
Brand: Packt Publishing
Price: 39.99 GBP
Availability: InStock

AI & Machine Learning

It has never been this cheap. We have no record of a lower price.

£40 today · cheaper than every other day in the last 3 months

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 32 days • 15 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 32 days • 2 price levels

Days at Price

Current Price

Price Analysis

Most common price: £40 (13 days, 86.7%)

Price range: £40 - £42

Price levels: 2 different prices over 15 days

Description

Become proficient in deriving insights from time-series data and analyzing a model’s performance 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 Description Machine learning has emerged as a powerful tool to understand hidden complexities in time-series datasets, which frequently need to be analyzed in areas as diverse as healthcare, economics, digital marketing, and social sciences. These datasets are essential for forecasting and predicting outcomes or for detecting anomalies to support informed decision making. This book covers Python basics for time-series and builds your understanding of traditional autoregressive models as well as modern non-parametric models. 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. Machine Learning for Time-Series with Python explains the theory behind several useful models and guides you in matching the right model to the right problem. The book also includes real-world case studies covering weather, traffic, biking, and stock market data. By the end of this book, you will be proficient in effectively analyzing time-series datasets with machine learning principles. 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 are looking to perform time-series analysis to effectively predict outcomes. Basic knowledge of the Python language is essential. Familiarity with statistics is desirable. Table of Contents Introduction to Time-Series with Python Time-Series Analysis with Python Preprocessing Time-Series Introduction to Machine Learning for Time Series Forecasting with Moving Averages and Autoregressive Models Unsupervised Methods for Time-Series Machine Learning Models for Time-Series Online Learning for Time-Series Probabilistic Models for Time-Series Deep Learning for Time-Series Reinforcement Learning for Time-Series Multivariate Forecasting

Key Features

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

Product type: ABIS_BOOK

Brand: Packt Publishing

Product Specifications

Brand: Packt Publishing
Format: paperback
ASIN: 1801819629
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 29 October 2021
Listed Since: 21 September 2021

Barcode

No barcode data available

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

It has never been this cheap. We have no record of a lower price.

Price History & Forecast

Price Distribution

Price Analysis

Description

Key Features

Product Specifications

Barcode

Similar Products You Might Like

Modern Time Series Forecasting Techniques For Predictive Analytics and Anomaly Detection: From Classical Foundations to Cutting-Edge Applications

Time Series Analysis with Python Cookbook: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation

Modern Time Series Forecasting with Python: Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas

Time Series Analysis with Python Cookbook: Practical recipes for the complete time series workflow, from modern data engineering to advanced forecasting and anomaly detection

Time Series Forecasting in Python

Wiley Time Series Analysis - Probability and Statistics Book

The Analysis of Time Series: Theory and Practice (Monographs on Statistics and Applied Probability)

Time Series for Data Science: Analysis and Forecasting (Chapman & Hall/CRC Texts in Statistical Science)

Time Series for Data Science: Analysis and Forecasting (Chapman & Hall/CRC Texts in Statistical Science)

Time Series Forecasting Using Generative AI: Leveraging AI for Precision Forecasting

Statistical Learning for Big Dependent Data (Wiley Series in Probability and Statistics)

Springer Time Series Analysis, Modeling and Applications Book

Time Series Analysis and Forecasting by Example: 301 (Wiley Series in Probability and Statistics)

Long-Memory Time Series: Theory and Methods (Wiley Series in Probability and Statistics)

Data Science in Theory and Practice: Techniques for Big Data Analytics and Complex Data Sets

Uncertainty Analysis in Econometrics with Applications: 200 (Advances in Intelligent Systems and Computing, 200)

Time Series Modelling with Unobserved Components

Applied Time Series Analysis and Forecasting with Python (Statistics and Computing)

Time Series Analysis: Forecasting and Control (Wiley Series in Probability and Statistics)

Time Series: Modeling, Computation, and Inference, Second Edition (Chapman & Hall/CRC Texts in Statistical Science)

Linear Time Series with MATLAB and OCTAVE (Statistics and Computing)

Time Series Analysis (Chapman & Hall/CRC Texts in Statistical Science)

Introduction to Time Series Modeling with Applications in R (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)

Introduction to Time Series Analysis and Forecasting (WILEY SERIES IN PROB & STATISTICS/see 1345/6,6214/5)