A Practical Approach to Timeseries Forecasting Using Python
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A Practical Approach to Timeseries Forecasting Using Python
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What you'll learn
Visualize and manipulate time series data using Python and key libraries
Build and tune ARIMA and SARIMA models for effective forecasting
Implement LSTM, BiLSTM, and GRU models for deep learning-based predictions
Design end-to-end forecasting pipelines for real-world datasets
Details to know
See how employees at top companies are mastering in-demand skills
There are 9 modules in this course
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A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Dive into the dynamic world of time series forecasting with this comprehensive and hands-on Python course. You’ll gain practical skills in data manipulation, visualization, and forecasting techniques—empowering you to uncover trends, identify patterns, and make predictions using real-world datasets. Whether you're preparing stock forecasts or tracking public health trends, you'll be equipped to apply advanced forecasting tools effectively. Your journey begins with the fundamentals of time series data and gradually builds through essential processing techniques, including decomposition, noise reduction, and feature engineering. As the course progresses, you’ll explore powerful statistical models such as ARIMA and SARIMA before moving into deep learning-based forecasting using LSTM, BiLSTM, and GRU models. Hands-on projects like COVID-19 case prediction, Microsoft stock forecasting, and birth rate trend analysis reinforce theoretical knowledge and provide you with ready-to-use code and workflows. Quizzes and real datasets at every step ensure a fully immersive learning experience. This course is ideal for data enthusiasts, analysts, and aspiring machine learning engineers. A basic understanding of Python programming and fundamental statistics is recommended. The course is best suited for learners at an intermediate level.
In this module, we will introduce you to the fundamental concepts of time series forecasting, the course structure, and how each section will build towards a comprehensive understanding of this field. You will also be introduced to your instructor and get an overview of what to expect by the end of this course.
What's included
3 videos1 reading
3 videos•Total 15 minutes
- Introduction to Time Series Forecast•3 minutes
- Introduction to Instructor•3 minutes
- Course Introduction•9 minutes
1 reading•Total 10 minutes
- Full Course Resources•10 minutes
In this module, we will dive deep into the different aspects of time series data, covering its features, types, and the stages involved in forecasting. You will also learn about the integration of machine learning and neural networks, such as RNNs, in time series prediction.
What's included
9 videos1 assignment
9 videos•Total 29 minutes
- Introduction to Time Series Forecasting•5 minutes
- Features of Time Series•3 minutes
- Types of Time Series Data•2 minutes
- Stages for Time Series Forecasting•5 minutes
- Data Manipulation in Time Series•4 minutes
- Data Processing for Time Series Forecasting•3 minutes
- Machine Learning Forecasting•2 minutes
- RNN Forecasting•2 minutes
- Projects to Be Covered•4 minutes
1 assignment•Total 15 minutes
- Motivation and Overview of Time Series Analysis - Assessment•15 minutes
In this module, we will focus on the essential skills needed to manipulate and visualize time series data using Python. You will learn how to slice, index, and visualize both single and multiple features to better understand time series datasets.
What's included
17 videos1 assignment
17 videos•Total 111 minutes
- Module Overview•3 minutes
- Packages Required to Execute Codes Error-Free•5 minutes
- Overview of Basic Plotting and Visualization•4 minutes
- Overview of Time Series Parameters•6 minutes
- Dependencies Installation and Dataset Overview•12 minutes
- Data Manipulation in Python•14 minutes
- Data Slicing and Indexing•5 minutes
- Basic Data Visualization with Single Time Series Feature•6 minutes
- Data Visualization with Multiple Time Series Features•8 minutes
- Data Visualization with Customized Features Selection•6 minutes
- Area Plots in Data Analysis•5 minutes
- Histogram with Single Feature•5 minutes
- Histogram Multiple Features•10 minutes
- Pie Charts•10 minutes
- Time Series Parameters•8 minutes
- Quiz Video•2 minutes
- Quiz Solution•2 minutes
1 assignment•Total 15 minutes
- Basics of Data Manipulation in Time Series - Assessment•15 minutes
In this module, we will cover key data processing tasks required to prepare your dataset for forecasting. You will work through stationarity checks, noise reduction, and resampling, all essential steps for building a reliable forecasting model.
What's included
16 videos1 assignment
16 videos•Total 117 minutes
- Module Overview•14 minutes
- Dataset Significance•4 minutes
- Dataset Overview•8 minutes
- Dataset Manipulation•4 minutes
- Data Pre-Processing•11 minutes
- RVT Models•4 minutes
- Automatic Time Series Decomposition•12 minutes
- Trend Using Moving Average Filter•13 minutes
- Seasonality Comparison•6 minutes
- Resampling•4 minutes
- Noise in Time Series•12 minutes
- Feature Engineering•7 minutes
- Stationarity in Time Series•8 minutes
- Handling Non-Stationarity in Time Series•5 minutes
- Quiz•1 minute
- Quiz Solution•4 minutes
1 assignment•Total 15 minutes
- Data Processing for Timeseries Forecasting - Assessment•15 minutes
In this module, we will introduce machine learning approaches for time series forecasting, including ARIMA and SARIMA models. You will learn to implement these techniques using Python and assess their effectiveness through evaluation metrics.
What's included
16 videos1 assignment
16 videos•Total 105 minutes
- Section Overview•7 minutes
- Data Preparation•14 minutes
- Auto Correlation and Partial Correlation•11 minutes
- Data Splitting•5 minutes
- Autoregression•5 minutes
- Autoregression in Python•12 minutes
- Moving Average and ARMA•4 minutes
- ARIMA•4 minutes
- ARIMA in Python•6 minutes
- Auto ARIMA in Python•9 minutes
- SARIMA•3 minutes
- SARIMA in Python•6 minutes
- Auto SARIMA in Python•6 minutes
- Future Predictions Using SARIMA•8 minutes
- Quiz•2 minutes
- Quiz Solution•2 minutes
1 assignment•Total 15 minutes
- Machine Learning in Time Series Forecasting - Assessment•15 minutes
In this module, we will focus on Recurrent Neural Networks (RNNs), specifically LSTM and BiLSTM models, for time series forecasting. You will explore how these deep learning models are applied and optimized for accurate predictions.
What's included
17 videos1 assignment
17 videos•Total 112 minutes
- Module Overview•7 minutes
- Important Parameters•6 minutes
- LSTM Models•10 minutes
- BiLSTM Models•4 minutes
- GRU Models•6 minutes
- Underfitting and Overfitting•8 minutes
- Model for Underfitting and Overfitting•8 minutes
- Model Evaluation for Underfitting and Overfitting•6 minutes
- Dataset Preparation and Scaling•8 minutes
- Dataset Reshaping•9 minutes
- LSTM Implementation on Dataset•7 minutes
- Time Series Forecasting (TSF) Using LSTM•7 minutes
- Graph for TSF Using LSTM•6 minutes
- LSTM Parameter Change and Stacked LSTM•9 minutes
- BiLSTM for Time Series Forecasting•8 minutes
- Quiz•1 minute
- Quiz Solution•2 minutes
1 assignment•Total 15 minutes
- Recurrent Neural Networks in Time Series Forecasting - Assessment•15 minutes
In this module, we will guide you through a hands-on project predicting COVID-19 positive cases using machine learning algorithms. You will process and analyze the dataset, followed by the implementation of ARIMA and SARIMA models for future predictions.
What's included
12 videos1 assignment
12 videos•Total 84 minutes
- Project Overview•8 minutes
- Dataset Overview•6 minutes
- Dataset Correlation•6 minutes
- Shape and NULL Check•4 minutes
- Dataset Index•5 minutes
- Visualize the Data•7 minutes
- Area Plot•6 minutes
- Autocorrelation, Standard Deviation, and Mean•11 minutes
- Stationarity Check•9 minutes
- ARIMA Implementation•8 minutes
- SARIMA Implementation•11 minutes
- Variations in SARIMA•5 minutes
1 assignment•Total 15 minutes
- Project 1: COVID-19 Positive Cases Prediction Using Machine Learning Algorithm - Assessment•15 minutes
In this project, we will focus on predicting Microsoft Corporation's stock prices using RNN models. You will learn how to preprocess the dataset, visualize data patterns, and use LSTM and BiLSTM models for stock price forecasting.
What's included
12 videos1 assignment
12 videos•Total 81 minutes
- Module Overview•3 minutes
- Data Analysis•6 minutes
- Data Visualization Line Plots•7 minutes
- Area Plots•3 minutes
- Auto Correlation, Standard Deviation, and Mean•8 minutes
- Stationarity Check•4 minutes
- Data Manipulation for Deep Learning•8 minutes
- Dataset Division•8 minutes
- LSTM Implementation and Errors•6 minutes
- LSTM Forecasting•15 minutes
- Stacked LSTM Forecasting•3 minutes
- BiLSTM and Stacked BiLSTM•9 minutes
1 assignment•Total 15 minutes
- Project 2: Microsoft Corporation Stock Prediction Using RNNs - Assessment•15 minutes
In this project, you will use deep learning techniques to forecast birth rates over time. You will analyze and manipulate the dataset, then apply advanced RNN models like LSTM and BiLSTM to predict future birth rate trends.
What's included
13 videos2 assignments
13 videos•Total 90 minutes
- Project Overview•4 minutes
- Dataset Overview•5 minutes
- Yearly Birth Distribution Plot and Birth Rate Plot•13 minutes
- Monthly Birth Distribution Plot and Birth Rate Plot•8 minutes
- Day-Wise and Date-Wise Birth Distribution Plot and Birth Rate Plot•10 minutes
- Birth Rate Range Plot•10 minutes
- Data Manipulation•4 minutes
- Stationarity Check•5 minutes
- Manipulation for Forecasting•4 minutes
- Scaling•3 minutes
- LSTM Forecasting•11 minutes
- Stacked LSTM and BiLSTM•11 minutes
- Course Conclusion•1 minute
2 assignments•Total 75 minutes
- Full Course Assessment•60 minutes
- Full Course Practice Assessment•15 minutes
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