A Practical Approach to Timeseries Forecasting using Python

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Course curriculum

1. Introduction to Instructor
2. Course Introduction
1. Time Series Introduction and Motivation
2. Features of Time Series
3. Types of Time Series Data
4. Stages For Time Series Forecasting
5. Data Manipulation Motivation
6. Data Processing for Time Series Motivation
7. Machine Learning Motivation
8. RNN Motivation
9. Projects to be Covered
1. Module Overview
2. Packages Installation
3. Overview of Basic Plotting and Visualization
4. Overview of Time Series Parameters
5. Dependencies Installation and Dataset Overview
6. Data Manipulation in Python
7. Data Slicing and Indexing
8. Basic Data Visualization with Single Time Series Feature
9. Data Visualization with Multiple Time Series Feature
10. Data Visualization with Customized Features Selection
11. Area Plots in Data Analysis
12. Histogram with Single Feature
13. Histogram Multiple Features
14. Pie Charts
15. Time Series Parameters
16. Quiz Video
17. Quiz Solution
1. Module Overview
2. Dataset Significance
3. Dataset Overview
4. Dataset Manipulation
5. Data Preprocessing
6. RVT Models
7. Automatic Time Series Decomposition
8. Trend using Moving Average Filter
9. Seasonality Comparison
10. Resampling
11. Noise in Time Series
12. Feature Engineering
13. Stationarity in Time Series
14. Handling Non- Stationarity in Time Series
15. Quiz
16. Quiz Solution
1. Section Overview
2. Data Prepration
3. Auto Correlation and Partial Correlation
4. Data Splitting
5. AutoRegression
6. AutoRegression in Python
7. Moving Average and ARMA
8. ARIMA
9. ARIMA in Python
10. AutoArima in Python
11. SARIMA
12. SARIMA in Python
13. AutoSARIMA in Python
14. Future Predictions using SARIMA
15. Quiz
16. Quiz Solution
1. Module Overview
2. Important Parameters
3. LSTM Models
4. BiLSTM Models
5. GRU Models
6. Concept of Underfitting and Overfitting
7. Model for Underfitting and Overfitting
8. Model Evaluation for Underfitting and Overfitting
9. DataSet Prepration and Scaling
10. Dataset Reshaping
11. LSTM Implementation on Dataset
12. Time Series Forecasting (TSF) using LSTM
13. Graph for TSF using LSTM
14. LSTM Parameter Change and Stacked LSTM
15. Bi-LSTM for Time Series Forecasting
16. Quiz
17. Quiz Solution
1. Project Overview
2. Dataset Overview
3. Dataset Correlation
4. Shape and NULL Check
5. Dataset Index
6. Visualize the Data
7. Area Plot
8. Autocorrelation, Std. Deviation and Mean
9. Stationarity Check
10. ARIMA Implementation
11. Sarima Implementation
12. Variations in SARIMA
1. Module Overview
2. Data Analysis
3. Data Visualization Line Plots
4. Area Plots
5. AutoCorrelation, Std. Deviation and Mean
6. Stationarity Check
7. Data Manipulation for Deep Learning
8. Dataset Division
9. LSTM Implementation and Errors
10. LSTM Forecasting
11. Stacked LSTM Forecasting
12. BiLSTM and Stacked BiLSTM
1. Project Overview
2. Dataset Overview
3. Yearly Birth Distibution Plot and Birth Rate Plot
4. Monthly Birth Distibution Plot and Birth Rate Plot
5. Daywise and Datewise Birth Distibution Plot and Birth Rate Plot
6. Bith Rate Range Plot
7. Data Manipulation
8. Stationarity Check
9. Manipulation for Forecasting
10. Scaling
11. LSTM Forecasting
12. Stacked LSTM and BiLSTM

About this course

$199.99
113 lessons
12.5 hours of video content

A Practical Approach to Timeseries Forecasting using Python

Course curriculum

Introduction

Motivation and Overview of Time Series Analysis

Basics of Data Manipulation in Time Series

Data Processing for Timeseries Forecasting

Machine Learning in Time Series Forecasting

Recurrent Neural Networks in Time Series Forecasting

Project 1 COVID-19 Positive Cases Prediction using Machine Learning Algorith

Project 2 Microsoft Corporation Stock Prediction using RNNs

Project 3 Birthrate Forecasting using RNNs with Advance Data Analysis

About this course