Description

Gain a thorough grasp of time series analysis and its effects, as well as practical tips on how to apply machine learning methods and build RNNs. Learn to train RNNs efficiently while taking crucial concepts such as overfitting and underfitting into account. The course offers a useful, hands-on manner for learning Python methods and principles.

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

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

Audience

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.

Approach

This is a comprehensive, easy-to-understand, self-explanatory, to-the-point, and practical course with live coding and three in-depth projects covering complete course contents.

Every module has engaging content; a completely practical approach is used along with brief theoretical concepts. At the end of every module, there will be a quiz, followed by its solution in the next video.

Key Features

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

Github Repo

https://github.com/PacktPublishing/A-Practical-Approach-to-Timeseries-Forecasting-using-Python

About the Author

AI Sciences

AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences. Their courses have successfully helped more than 100,000 students master AI and data science.

Course Outline

1. Introduction

1. Introduction to Time Series Forecast

This video provides an introduction to the time series forecast.

2. Introduction to Instructor

This video provides an introduction to the instructor in detail. You will look at the course's structure and the things you will learn by the end of this course.

3. Course Introduction

This video talks about an overview of the course and its learning outcomes.

2. Motivation and Overview of Time Series Analysis

1. Introduction to Time Series Forecasting

This video helps you with an introduction to time series forecasting.

2. Features of Time Series

This video explains the features of time series.

3. Types of Time Series Data

This video explains different types of time series data. First, you will see univariate and then multivariate time series data.

4. Stages for Time Series Forecasting

This video explains the stages of time series data analysis and forecasting.

5. Data Manipulation in Time Series

This video talks about basic data manipulation in time series.

6. Data Processing for Time Series Forecasting

This video helps you with data processing for time series forecasting.

7. Machine Learning Forecasting

This video explains machine learning in time series forecasting.

8. RNN Forecasting

This video explains recurrent neural networks (RNNs) for time series forecasts.

9. Projects to Be Covered

This video helps in discussing the different projects that will be worked on in this course.

3. Basics of Data Manipulation in Time Series

1. Module Overview

This video helps you with an overview of the module.

2. Packages Required to Execute Codes Error-Free

This video helps you with the installation of the packages.

3. Overview of Basic Plotting and Visualization

This video provides an overview of basic plotting and visualization.

4. Overview of Time Series Parameters

This video provides an overview of time series parameters.

5. Dependencies Installation and Dataset Overview

This video provides an overview of dependencies installation and the dataset.

6. Data Manipulation in Python

This video talks about data manipulation in Python.

7. Data Slicing and Indexing

This video explains data slicing and indexing in detail.

8. Basic Data Visualization with Single Time Series Feature

This video explains basic data visualization with single time series features.

9. Data Visualization with Multiple Time Series Features

This video explains data visualization with multiple time series features.

10. Data Visualization with Customized Features Selection

This video demonstrates data visualization with customized features selection.

11. Area Plots in Data Analysis

This video explains how to plot areas using data analysis.

12. Histogram with Single Feature

This video showcases histogram with a single feature.

13. Histogram Multiple Features

This video showcases and explains the multiple features of histogram in detail.

14. Pie Charts

This video explains pie charts and their use.

15. Time Series Parameters

This video explains the time series parameters.

16. Quiz Video

This is a quiz video on data manipulation.

17. Quiz Solution

This is a solution video of the quiz on data manipulation.

4. Data Processing for Timeseries Forecasting

1. Module Overview

This video provides an overview of the section.

2. Dataset Significance

This video talks about the significance of datasets.

3. Dataset Overview

This video provides an overview of the dataset.

4. Dataset Manipulation

This video explains how to do dataset manipulation.

5. Data Pre-Processing

This video helps you with data pre-processing.

6. RVT Models

This video explains the RVT (Resampling, Visualize, and Transform) models in time series in Python.

7. Automatic Time Series Decomposition

This video explains how to execute automatic time series decomposition.

8. Trend Using Moving Average Filter

This video extracts the trend using the moving average filter.

9. Seasonality Comparison

This video explains the seasonality comparison and here, you will actually execute the seasonality comparison.

10. Resampling

This video explains resampling and helps you with resampling your dataset.

11. Noise in Time Series

This video explains about noise in time series and executes ways to reduce noise in your dataset.

12. Feature Engineering

This video explains the concept of feature engineering.

13. Stationarity in Time Series

This video talks about stationarity in time series. It is nothing but a series whose properties do not depend on the time at which the series is observed.

14. Handling Non-Stationarity in Time Series

This video explains how to handle non-stationarity in time series.

15. Quiz

This is a quiz video on data processing.

16. Quiz Solution

This is a solution video of the quiz on data processing.

5. Machine Learning in Time Series Forecasting

1. Section Overview

This video provides an overview of the section.

2. Data Preparation

This video demonstrates how to get your data prepared before any operation.

3. Auto Correlation and Partial Correlation

This video explains the auto correlation and partial correlation properties.

4. Data Splitting

This video helps you with data splitting. It will help you concise your big dataset easily.

5. Autoregression

This video focuses on autoregression. It is a subset of time series models, which can be used to predict future values based on previous observations.

6. Autoregression in Python

This video covers autoregression using python.

7. Moving Average and ARMA

This video explains moving average and ARMA (Autoregressive Moving Average Model).

8. ARIMA

This video explains ARIMA (Autoregressive Integrated Moving Average Model) and how it is a better model than ARMA.

9. ARIMA in Python

This video helps you with executing ARIMA in Python.

10. Auto ARIMA in Python

This video talks about Auto ARIMA and how it is one of the best models in time series forecasting.

11. SARIMA

This video explains SARIMA (Seasonal Autoregressive Integrated Moving Average).

12. SARIMA in Python

This video helps you execute SARIMA in Python.

13. Auto SARIMA in Python

This video helps you execute Auto SARIMA in Python.

14. Future Predictions Using SARIMA

This video helps you with future predictions using SARIMA.

15. Quiz

This is a quiz video on machine learning in time series forecasting.

16. Quiz Solution

This is a solution video of the quiz on machine learning in time series forecasting.

6. Recurrent Neural Networks in Time Series Forecasting

1. Module Overview

This video provides an overview of the section.

2. Important Parameters

This video talks about the important parameters in time series forecasting.

3. LSTM Models

This video explains the LSTM models in detail.

4. BiLSTM Models

This video explains the BiLSTM models in detail.

5. GRU Models

This video explains the GRU (Gated Recurrent Unit) models and their application.

6. Underfitting and Overfitting

This video explains the concepts of underfitting and overfitting.

7. Model for Underfitting and Overfitting

This video demonstrates the model for underfitting and overfitting.

8. Model Evaluation for Underfitting and Overfitting

This video demonstrates model evaluation for underfitting and overfitting.

9. Dataset Preparation and Scaling

This video helps you with dataset preparation and scaling from scratch.

10. Dataset Reshaping

This video explains how to reshape the data and extract the intended information.

11. LSTM Implementation on Dataset

This video demonstrates LSTM implementation on the dataset.

12. Time Series Forecasting (TSF) Using LSTM

This video explains the implementation of Time Series Forecasting (TSF) using LSTM.

13. Graph for TSF Using LSTM

This video explains the graph for TSF using LSTM.

14. LSTM Parameter Change and Stacked LSTM

This video explains LSTM Parameter Change and Stacked LSTM.

15. BiLSTM for Time Series Forecasting

This video talks about BiLSTM for time series forecasting.

16. Quiz

This is a quiz video on RNN in time series forecasting.

17. Quiz Solution

This is a solution video of the quiz on RNN in time series forecasting.

7. Project 1: COVID-19 Positive Cases Prediction Using Machine Learning Algorithm

1. Project Overview

This video provides an overview of the project.

2. Dataset Overview

This video helps you with an overview of the dataset.

3. Dataset Correlation

This video explains the correlation pattern in the dataset.

4. Shape and NULL Check

This video executes the Shape and NULL Check operations on the dataset.

5. Dataset Index

This video executes the indexing of the dataset.

6. Visualize the Data

This video helps you with visualizing the data.

7. Area Plot

This video demonstrates plotting of the area of the dataset.

8. Autocorrelation, Standard Deviation, and Mean

This video teaches you how to execute the dataset to find its autocorrelation, standard deviation, and mean.

9. Stationarity Check

This video will do the stationarity check on your data.

10. ARIMA Implementation

This video will help you with the implementation of ARIMA.

11. SARIMA Implementation

This video helps you with SARIMA implementation.

12. Variations in SARIMA

This video demonstrates variations in SARIMA with the dataset.

8. Project 2: Microsoft Corporation Stock Prediction Using RNNs

1. Module Overview

This video provides an overview of the project.

2. Data Analysis

This video helps you with the data analysis part.

3. Data Visualization Line Plots

This video demonstrates data visualization using line plots.

4. Area Plots

This video demonstrates plotting of the area of the dataset.

5. Auto Correlation, Standard Deviation, and Mean

This video explains and executes the dataset's autocorrelation, standard deviation, and mean.

6. Stationarity Check

This video will do the stationarity check on your data.

7. Data Manipulation for Deep Learning

This video displays how to do data manipulation using a deep-learning model.

8. Dataset Division

This video explains how to do dataset division.

9. LSTM Implementation and Errors

This video explains the implementation of LSTM and its errors.

10. LSTM Forecasting

This video helps you with LSTM forecasting.

11. Stacked LSTM Forecasting

This video helps you with the Stacked LSTM forecasting.

12. BiLSTM and Stacked BiLSTM

This video helps you with the execution of BiLSTM and Stacked BiLSTM on data.

9. Project 3: Birth Rate Forecasting Using RNNs with Advanced Data Analysis

1. Project Overview

This video provides an overview of the project.

2. Dataset Overview

This video helps you with an overview of the dataset.

3. Yearly Birth Distribution Plot and Birth Rate Plot

This video demonstrates the plotting of the yearly birth distribution and birth rate.

4. Monthly Birth Distribution Plot and Birth Rate Plot

This video demonstrates the plotting of the monthly birth distribution and birth rate.

5. Day-Wise and Date-Wise Birth Distribution Plot and Birth Rate Plot

This video demonstrates the plotting of day-wise and date-wise birth distribution and birth rate.

6. Birth Rate Range Plot

This video helps you plot the birth rate range.

7. Data Manipulation

This video displays how to do data manipulation using a deep learning model.

8. Stationarity Check

This video will do the stationarity check on your data.

9. Manipulation for Forecasting

This video displays how to do data manipulation for forecasting.

10. Scaling

This video helps you with the scaling of the dataset.

11. LSTM Forecasting

This video talks about LSTM forecasting.

12. Stacked LSTM and BiLSTM

This video implements Stacked LSTM and BiLSTM on data.

13. Course Conclusion

This video helps you with the outro of the course and asks you to write your honest reviews about the course.

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A Practical Approach to Timeseries Forecasting Using Python

By Packt

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