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