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This is the code repository for Modern Time Series Forecasting with Python, published by Packt.
Explore industry-ready time series forecasting using modern machine learning and deep learning
We 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 book covers the following exciting features:
If you feel this book is for you, get your copy today!
<img src="https://raw.githubusercontent.com/PacktPublishing/GitHub/master/GitHub.png" alt="https://www.packtpub.com/" border="5" />
All of the code is organized into folders.
The code will look like the following:
#Does not support missing values, so using imputed ts instead
res = seasonal_decompose(ts, period=7*48, model="additive",
extrapolate_trend="freq")
Following is what you need for this book: 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.
The easiest way to setup the environment is by using Anaconda, a distribution of Python for scientific computing. You can use Miniconda, a minimal installer for conda as well if you do not want the pre-installed packages that come with Anaconda.
conda activate
conda activate
cd
.conda env create -f anaconda_env.yml
This creates a new environment under the name, modern_ts
, and will install all the required libraries in the environment. This can take a while.conda activate modern_ts
and then use Jupyter Notebook (jupyter notebook
) or Jupyter Lab (jupyter lab
) according to your preference.Sometimes the anaconda installation can stall at Solving Environment
. This is because anaconda can sometimes be really slow at resolving package dependencies. We can get around this by using Mamba
.
Mamba
is a fast, robust, and cross-platform package manager.
It runs on Windows, OS X and Linux (ARM64 and PPC64LE included) and is fully compatible with conda packages and supports most of conda’s commands.
All we need to do is:
conda install mamba -n base -c conda-forge
mamba env create -f anaconda_env.yml
If the installation doesn't work for MacOS, please try the following:
anaconda_env.yml
, change the line python-kaleido==0.1.0
to python-kaleido>=0.1.0
anaconda_env.yml
, change the line statsforecast==0.6.0
to statsforecast>=0.6.0
Now, try installing the environment again. If this doesn't work, please raise an issue on the GitHub repo.
You are going to be using a single dataset throughout the book. The book uses London Smart Meters Dataset from Kaggle for this purpose. Therefore, if you don’t have an account with Kaggle, please go ahead and make one. https://www.kaggle.com/account/login?phase=startRegisterTab There are two ways you can download the data- automated and manual. For the automated way, we need to download a key from Kaggle. Let’s do that first (if you are going to choose the manual way, you can skip this).
data
├── london_smart_meters
│ ├── hhblock_dataset
│ │ ├── hhblock_dataset
│ │ ├── block_0.csv
│ │ ├── block_1.csv
│ │ ├── ...
│ │ ├── block_109.csv
│ │── acorn_details.csv
│ ├── informations_households.csv
│ ├── uk_bank_holidays.csv
│ ├── weather_daily_darksky.csv
│ ├── weather_hourly_darksky.csv
There can be additional files as part of the extraction process. You can remove them without impacting anything. There is a helpful script which checks this structure. python test_data_download.py
Number of blocks to select from the dataset is dependent on how much RAM you have in your machine. Although, these are not rules, but rough guidelines on how much blocks to choose based on your RAM is given below. If you still face problems, please experiment with lowering the number of blocks to make it work better for you.
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
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Manu Joseph is a self-made data scientist with more than a decade of experience working with many Fortune 500 companies, enabling digital and AI transformations, specifically in machine learningbased demand forecasting. He is considered an expert, thought leader, and strong voice in the world of time series forecasting. Currently, Manu leads applied research at Thoucentric, where he advances research by bringing cutting-edge AI technologies to the industry. He is also an active open source contributor and has developed an open source library—PyTorch Tabular—which makes deep learning for tabular data easy and accessible. Originally from Thiruvananthapuram, India, Manu currently resides in Bengaluru, India, with his wife and son.
If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.
https://packt.link/free-ebook/9781803246802
On page 3 of the Book, In chapter 1, it should be Welcome to "Modern Time Series Forecasting with Python" instead of Welcome to "Advanced Time Series Analysis Using Python".