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A Practical Approach to Timeseries Forecasting Using Python
A Practical Approach to Timeseries Forecasting Using Python

A Practical Approach to Timeseries Forecasting Using Python: Learn Time Series Forecasting Using Machine Learning, Recursive Neural Networks, and Python

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€18.99 per month
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.5 (2 Ratings)
Video Mar 2023 12hrs 25mins 1st Edition
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€82.99
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Renews at €18.99p/m
Arrow left icon
Profile Icon AI Sciences
Arrow right icon
€18.99 per month
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.5 (2 Ratings)
Video Mar 2023 12hrs 25mins 1st Edition
Video
€82.99
Subscription
Free Trial
Renews at €18.99p/m
Video
€82.99
Subscription
Free Trial
Renews at €18.99p/m

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Key benefits

  • Complete package for beginners to learn time series, data analysis, and forecasting methods from scratch
  • Thoroughly covers the most advanced and recently discovered RNN models
  • Analysis on real-world datasets of birth rates, stock exchange and COVID-19 cases

Description

Have you ever wondered how weather predictions, population estimates, and even the lifespan of the universe are made? Discover the power of time series forecasting with state-of-the-art ML and DL models. The course begins with the fundamentals of time series analysis, including its characteristics, applications in real-world scenarios, and practical examples. Then progress to exploring data analysis and visualization techniques for time series data, ranging from basic to advanced levels, using powerful libraries such as NumPy, Pandas, and Matplotlib. Python will be utilized to assess various aspects of your time series data, such as seasonality, trend, noise, autocorrelation, mean over time, correlation, and stationarity. Additionally, you will learn how to pre-process time series data for utilization in applied machine learning and recurrent neural network models, which will enable you to train, test, and assess your forecasted results. Finally, you will acquire an understanding of the applied ML models, including their performance evaluations and comparisons. In the RNNs module, you will be building GRU, LSTM, Stacked LSTM, BiLSTM, and Stacked BiLSTM models. By the end of this course, you will be able to understand time series forecasting and its parameters, evaluate the ML models, and evaluate the model and implement RNN models for time series forecasting. All the resource files are added to the GitHub repository at: https://github.com/PacktPublishing/A-Practical-Approach-to-Timeseries-Forecasting-using-Python

Who is this book for?

No prior knowledge of DL, data analysis, or math is required. You will start from the basics and gradually build your knowledge of the subject. Only the basics of Python are required. This course is designed for both beginners with some programming experience and even those who know nothing about data analysis, ML, and RNNs. The course is suitable for individuals who want to advance their skills in ML and DL, master the relation of data science with time series analysis, implement time series parameters and evaluate their impact on it and implement ML algorithms for time series forecasting.

What you will learn

  • Learn data analysis techniques and handle time series forecasting
  • Implement data visualization techniques using Matplotlib
  • Evaluate applied machine learning in time series forecasting
  • Look at auto regression, ARIMA, Auto ARIMA, SARIMA, and SARIMAX
  • Learn to model LSTM, Stacked LSTM, BiLSTM, and Stacked BiLSTM models
  • Implement ML and RNN models with three state-of-the-art projects

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Mar 13, 2023
Length: 12hrs 25mins
Edition : 1st
Language : English
ISBN-13 : 9781837632510
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What do you get with a Packt Subscription?

Free for first 7 days. $19.99 p/m after that. Cancel any time!
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Product feature icon 50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.
Product feature icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Product feature icon Thousands of reference materials covering every tech concept you need to stay up to date.
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Product Details

Publication date : Mar 13, 2023
Length: 12hrs 25mins
Edition : 1st
Language : English
ISBN-13 : 9781837632510
Category :
Languages :
Tools :

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Table of Contents

9 Chapters
Introduction Chevron down icon Chevron up icon
Motivation and Overview of Time Series Analysis Chevron down icon Chevron up icon
Basics of Data Manipulation in Time Series Chevron down icon Chevron up icon
Data Processing for Timeseries Forecasting Chevron down icon Chevron up icon
Machine Learning in Time Series Forecasting Chevron down icon Chevron up icon
Recurrent Neural Networks in Time Series Forecasting Chevron down icon Chevron up icon
Project 1: COVID-19 Positive Cases Prediction Using Machine Learning Algorithm Chevron down icon Chevron up icon
Project 2: Microsoft Corporation Stock Prediction Using RNNs Chevron down icon Chevron up icon
Project 3: Birth Rate Forecasting Using RNNs with Advanced Data Analysis Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.5
(2 Ratings)
5 star 0%
4 star 0%
3 star 50%
2 star 50%
1 star 0%
Gerardo Feb 18, 2024
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
Good initial review. But, lacks proper explanation of some parts like how to read the trend, seasonality and noise plots. Or, why do we need stationarity in our data. Also, not all datasets that uses are available in the download package, for example, the dataset for the data processing for timeseries quiz
Subscriber review Packt
Amanda Apr 11, 2024
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
It is really difficult to understand what he is saying and he doesn't really explain any of the concepts in the first few videos. He just shows some examples of what constitutes as different series
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