Course curriculum
-
1
Introduction
- Introduction to Instructor
- Course Introduction
-
2
Motivation and Overview of Time Series Analysis
- Time Series Introduction and Motivation
- Features of Time Series
- Types of Time Series Data
- Stages For Time Series Forecasting
- Data Manipulation Motivation
- Data Processing for Time Series Motivation
- Machine Learning Motivation
- RNN Motivation
- Projects to be Covered
-
3
Basics of Data Manipulation in Time Series
- Module Overview
- Packages Installation
- Overview of Basic Plotting and Visualization
- Overview of Time Series Parameters
- Dependencies Installation and Dataset Overview
- Data Manipulation in Python
- Data Slicing and Indexing
- Basic Data Visualization with Single Time Series Feature
- Data Visualization with Multiple Time Series Feature
- Data Visualization with Customized Features Selection
- Area Plots in Data Analysis
- Histogram with Single Feature
- Histogram Multiple Features
- Pie Charts
- Time Series Parameters
- Quiz Video
- Quiz Solution
-
4
Data Processing for Timeseries Forecasting
- Module Overview
- Dataset Significance
- Dataset Overview
- Dataset Manipulation
- Data Preprocessing
- RVT Models
- Automatic Time Series Decomposition
- Trend using Moving Average Filter
- Seasonality Comparison
- Resampling
- Noise in Time Series
- Feature Engineering
- Stationarity in Time Series
- Handling Non- Stationarity in Time Series
- Quiz
- Quiz Solution
-
5
Machine Learning in Time Series Forecasting
- Section Overview
- Data Prepration
- Auto Correlation and Partial Correlation
- Data Splitting
- AutoRegression
- AutoRegression in Python
- Moving Average and ARMA
- ARIMA
- ARIMA in Python
- AutoArima in Python
- SARIMA
- SARIMA in Python
- AutoSARIMA in Python
- Future Predictions using SARIMA
- Quiz
- Quiz Solution
-
6
Recurrent Neural Networks in Time Series Forecasting
- Module Overview
- Important Parameters
- LSTM Models
- BiLSTM Models
- GRU Models
- Concept of Underfitting and Overfitting
- Model for Underfitting and Overfitting
- Model Evaluation for Underfitting and Overfitting
- DataSet Prepration and Scaling
- Dataset Reshaping
- LSTM Implementation on Dataset
- Time Series Forecasting (TSF) using LSTM
- Graph for TSF using LSTM
- LSTM Parameter Change and Stacked LSTM
- Bi-LSTM for Time Series Forecasting
- Quiz
- Quiz Solution
-
7
Project 1 COVID-19 Positive Cases Prediction using Machine Learning Algorith
- Project Overview
- Dataset Overview
- Dataset Correlation
- Shape and NULL Check
- Dataset Index
- Visualize the Data
- Area Plot
- Autocorrelation, Std. Deviation and Mean
- Stationarity Check
- ARIMA Implementation
- Sarima Implementation
- Variations in SARIMA
-
8
Project 2 Microsoft Corporation Stock Prediction using RNNs
- Module Overview
- Data Analysis
- Data Visualization Line Plots
- Area Plots
- AutoCorrelation, Std. Deviation and Mean
- Stationarity Check
- Data Manipulation for Deep Learning
- Dataset Division
- LSTM Implementation and Errors
- LSTM Forecasting
- Stacked LSTM Forecasting
- BiLSTM and Stacked BiLSTM
-
9
Project 3 Birthrate Forecasting using RNNs with Advance Data Analysis
- Project Overview
- Dataset Overview
- Yearly Birth Distibution Plot and Birth Rate Plot
- Monthly Birth Distibution Plot and Birth Rate Plot
- Daywise and Datewise Birth Distibution Plot and Birth Rate Plot
- Bith Rate Range Plot
- Data Manipulation
- Stationarity Check
- Manipulation for Forecasting
- Scaling
- LSTM Forecasting
- Stacked LSTM and BiLSTM