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Advanced Forecasting with Python: With State-of-the-A
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eBay item number:387016719283
Item specifics
- Condition
- ISBN-13
- 9781484271490
- Book Title
- Advanced Forecasting with Python
- ISBN
- 9781484271490
- Subject Area
- Computers, Science, Mathematics
- Publication Name
- Advanced Forecasting with Python : With State-Of-the-Art-Models Including LSTMs, Facebook's Prophet, and Amazon's DeepAR
- Publisher
- Apress L. P.
- Item Length
- 10 in
- Subject
- Probability & Statistics / General, Intelligence (Ai) & Semantics, General, Programming Languages / Python
- Publication Year
- 2021
- Type
- Textbook
- Format
- Trade Paperback
- Language
- English
- Item Weight
- 21.3 Oz
- Item Width
- 7 in
- Number of Pages
- Xvii, 296 Pages
About this product
Product Information
Cover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Facebook's open-source Prophet model, and Amazon's DeepAR model. Rather than focus on a specific set of models, this book presents an exhaustive overview of all the techniques relevant to practitioners of forecasting. It begins by explaining the different categories of models that are relevant for forecasting in a high-level language. Next, it covers univariate and multivariate time series models followed by advanced machine learning and deep learning models. It concludes with reflections on model selection such as benchmark scores vs. understandability of models vs. compute time, and automated retraining and updating of models. Each of the models presented in this book is covered in depth, with an intuitive simple explanation of the model, a mathematical transcription of the idea, and Python code that applies the model to an example data set. Reading this book will add a competitive edge to your current forecasting skillset. The book is also adapted to those who have recently started working on forecasting tasks and are looking for an exhaustive book that allows them to start with traditional models and gradually move into more and more advanced models. What You Will Learn Carry out forecasting with Python Mathematically and intuitively understand traditional forecasting models and state-of-the-art machine learning techniques Gain the basics of forecasting and machine learning, including evaluation of models, cross-validation, and back testing Select the right model for the right use case Who This Book Is For The advanced nature of the later chapters makes the book relevant for applied experts working in the domain of forecasting, as the models covered have been published only recently. Experts working in the domain will want to update their skills as traditional models are regularly being outperformed by newer models.
Product Identifiers
Publisher
Apress L. P.
ISBN-10
1484271491
ISBN-13
9781484271490
eBay Product ID (ePID)
2321443703
Product Key Features
Number of Pages
Xvii, 296 Pages
Language
English
Publication Name
Advanced Forecasting with Python : With State-Of-the-Art-Models Including LSTMs, Facebook's Prophet, and Amazon's DeepAR
Publication Year
2021
Subject
Probability & Statistics / General, Intelligence (Ai) & Semantics, General, Programming Languages / Python
Type
Textbook
Subject Area
Computers, Science, Mathematics
Format
Trade Paperback
Dimensions
Item Weight
21.3 Oz
Item Length
10 in
Item Width
7 in
Additional Product Features
Dewey Edition
23
Number of Volumes
1 Vol.
Illustrated
Yes
Dewey Decimal
006.31
Lc Classification Number
Q325.5-.7
Table of Content
Chapter 1: Models for Forecasting.- Chapter 2: Model Evaluation for Forecasting.- Chapter 3: The AR Model.- Chapter 4: The MA model.- Chapter 5: The ARMA model.- Chapter 6: The ARIMA model.- Chapter 7: The SARIMA Model.- Chapter 8: The VAR model.- Chapter 9: The Bayesian VAR model.- Chapter 10: The Linear Regression model.- Chapter 11: The Decision Tree model.- Chapter 12: The k-Nearest Neighbors VAR model.- Chapter 13: The Random Forest Model.- Chapter 14: The XGBoost model.- Chapter 15: The Neural Network model.- Chapter 16: Recurrent Neural Networks.- Chapter 17: LSTMs.- Chapter 18: Facebook's Prophet model.- Chapter 19: Amazon's DeepAR Model.- Chapter 20: Deep State Space Models.- Chapter 21: Model selection.
Copyright Date
2021
Item description from the seller
Seller assumes all responsibility for this listing.
eBay item number:387016719283
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Item as described, good price, well packaged, arrived slightly later than hoped (not in time for Christmas ) but the order was placed in a very busy shipping period. No issue with the seller’s speed of response and sending the item. Great seller!
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Excellent seller. Timely shipping, safe packing, good communication, great price and as described. Thank you. A+++
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The seller is one of the best there is - high quality books, reasonable prices, securely packaged.
I do, however, recommend some changes to their shipping - they show a shipper (SortHub) on eBay, but the tracking # they email is for a different carrier. It’s difficult to know where my purchase is.
This time, it was delivered a week late. In the grand scheme of things not a big deal. But it shouldn’t be this difficult to accurately track my purchase.
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