Machine Learning for Time-Series with Python.Published by Packt

Last update: Dec 28, 2022

Overview

Machine-Learning-for-Time-Series-with-Python

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 The updated edition enables you to implement evergreen frameworks that will stay relevant as Power BI updates. Get familiar with Power BI development tools and services by going deep into the data connectivity, transformation, modeling, visualization, and analytical capabilities of Power BI. Microsoft Power BI Cookbook, Second Edition enables Power BI’s functional programming languages of DAX and M to come alive to deliver powerful solutions to common business intelligence challenges.

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.

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

Author Notes

I've heard from a few people struggling with tsfresh and featuretools for chapter 3.

My PR for tsfresh was merged mid-December fixing a version incompatibility - featuretools went through many breaking changes with the release of version 1.0.0 (congratulations to the team!). Please see how to fix any problems in the discussion here.

Comments

the code is not complete

Hi Ben,

I have high expectation on your book and am very excited to learn. However, the codes published on Github is incomplete. For example, in chapter 2, where are the codes from page 52 to 53 especially page 53? I am very interested to know how you did the code in page 53. Also some codes in jupyter notebook are not explained either in notebook and book. I am kind of disappointed. Could you please republish your complete jupyter notebook? Thanks

opened by yongnuan 14
Chapter 3 Preprocessing notebook

Hello, In the Chapter 3 Preprocessing notebook there seem to be several errors in the code.

When installing 'featuretools[tsfresh]' in colab the following error occurs: WARNING: featuretools 0.27.1 does not provide the extra 'tsfresh'

When running the first code bracket using feature tools in colab the following error occurs: TypeError: import_optional_dependency() got an unexpected keyword argument 'errors'

It appears colab installs featuretools version 0.27.1 If I install the most recent version of featuretoolls (i.e., 1.3.0) and run the code bracket below Automated Feature Extraction I get the following error: AttributeError: 'EntitySet' object has no attribute 'entity_from_dataframe'

If it is versioning that is an issue, please let me know what version of featuretools the code in the book was based on. If it is not versioning, would you be able to advise what the issue is?

Let me know if you have any questions. Thanks!

Best, Nils

opened by nbwosm 3
this repo's minor readme issue

Hi Ben!

Not a book issue, but one with the readme associated with this repo. The Key Features section starts fine but in the middle it accidentally switches into a description of another Pack book on Power BI. Looking forward to reading your book!

Alex

opened by TacheAlexandru 1
Fix chapters 6 order as per book and fix a run time issue.
Change the order for chapter-6 as per the book.

issue in the notebook for line intrusion_outliers = od.predict(intrusions["data"][:,0])

Few minor modifications for the chapter 5 notebook.
opened by sharanharsoor 0
Update parameter use in get_season() function

An integer is being used instead of the yearly_periods parameter in the get_season() function. The function works as is, but is not generalizable to seasonality of other yearly_periods values.

opened by JTanner4 0
Hard to believe these are typos

This is from the documentation of ARCH package "The null hypothesis of the Augmented Dickey-Fuller is that there is a unit root, with the alternative that there is no unit root. If the p-value is above a critical size, then the null cannot be rejected that there and the series appears to be a unit root."

on p. 152 a p-value of 0.997 is reported (in a figure which references KPSS test while the table shown clearly says that it is an ADF test) and the following conclusion is made "Given the p-value of 0.997, we can reject our null hypothesis of the unit root, and we conclude that our process is weakly stationary."

In a couple of pages "Please note that we need to set trend="t" here so that the model includes a constant. If not, we would get a spurious regression." In the statsmodels' ARIMA class documentation, it is stated clearly that trend is "Parameter controlling the deterministic trend. Can be specified as a string where ‘c’ indicates a constant term, ‘t’ indicates a linear trend in time, and ‘ct’ includes both."

It's quite doubtful that the author of this book understands what he is writing about. But at least he could have checked the documentation of the packages used in it.

opened by niskrev 1
Time Series Clustering

The coverage of time series clustering is poor. Actually does not exist beyond referencing a few libraries which people can find themselves. Any follow up with examples here would help I think.

opened by ghost 3

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