Mastering Time Series Forecasting with Python

Course Curriculum

Chapter 1: Introduction

Lecture 1: What is Time Series Data

Lecture 2: Time Series Components

Lecture 3: Download the Resources

Lecture 4: Good Learning Practice

Chapter 2: Setting Google Colab

Lecture 1: Install Google Colab to your mail id

Lecture 2: Integrate Google Drive to Colab to Load Data

Chapter 3: Time Series Visualizations

Lecture 1: Download the Resources

Lecture 2: Types of Charts for Time Series

Lecture 3: Setting up Google Colab

Lecture 4: Load the Data

Lecture 5: Line Chart

Lecture 6: Hue the Line Chart

Lecture 7: Area Chart

Lecture 8: Bar Plot

Lecture 9: Proposition and Stacked Bar, Area Chart

Lecture 10: Heatmaps

Chapter 4: Linear Regression Intution

Lecture 1: Download the Resources

Lecture 2: Intuition of Linear Regression

Lecture 3: Exploratory Data Analysis

Lecture 4: EDA – Quantitative Technique

Lecture 5: EDA – Graphical Technique

Lecture 6: Simple Linear Regression – Python

Lecture 7: Simple Linear Regression – Sklearn (Python)

Lecture 8: Simple Linear Regression – Statsmodels (Python)

Lecture 9: Model Evaluation – R^2, ANOVA

Lecture 10: Model Evaluation – Python

Chapter 5: Regression for Time Series Forecasting

Lecture 1: Regression with Time

Lecture 2: Download the Resources

Lecture 3: Data Preprocessing in Python

Lecture 4: Splitting Data into Training and Testing Sets in Python

Lecture 5: Train Regression Model with Time in Python

Lecture 6: Forecasting with Confidence Interval and Visualizations in Python

Chapter 6: Additive Time Series Model with Statsmodels

Lecture 1: Additive Model

Lecture 2: Data Analysis in Python

Lecture 3: Creating Seasonal Features

Lecture 4: Splitting Data into Training and Testing Sets

Lecture 5: Training Additive Model in Statsmodels

Lecture 6: Additive Model Forecasting and Visualizations

Chapter 7: Multiplicative Time Series Model

Lecture 1: Multiplicative Model

Lecture 2: Step-1: Trend Model

Lecture 3: Step-2: Calculate Seasonal Deviation

Lecture 4: Step-3: Seasonal Corrector Factor

Lecture 5: Fitted values and Forecasting with Multiplicative Model

Lecture 6: Margin of Error and Confidence Interval

Lecture 7: Visualizing Forecasted Data

Chapter 8: Auto Regressive Methods

Lecture 1: Auto Regressive Methods

Lecture 2: Download the Resources

Lecture 3: Setting Up for Model Building

Lecture 4: Data Preprocessing

Lecture 5: ACF & PACF

Lecture 6: Making Data Stationary

Lecture 7: Training AR Model

Lecture 8: Fitted and Forecasting values with AR Model

Lecture 9: AR Model Evaluation

Chapter 9: Smoothing Methods (Moving Average)

Lecture 1: Smoothing Techniques

Lecture 2: Download the Resources

Lecture 3: Naive Forecasting Model

Lecture 4: Naive Forecasting Model in Python – part 1

Lecture 5: Naive Forecasting Model in Python – part 2

Lecture 6: Simple Moving Average

Lecture 7: Simple Moving Average in Python

Lecture 8: Simple Moving Average order (q) in Python

Lecture 9: Weighted Moving Average

Lecture 10: Weighted Moving Average in Python

Lecture 11: Exponential Moving Average

Lecture 12: Exponential Moving Average in Python

Chapter 10: Non Seasonal ARIMA models

Lecture 1: ARMA

Lecture 2: Non Seasonal ARIMA

Lecture 3: Downloads Data and Notebook

Lecture 4: ARMA – Load Data

Lecture 5: ARMA – Split the Data into train and test sets

Lecture 6: ARMA – Steps to Build the Models

Lecture 7: ARMA – Augmented Dickey Fuller test for stationary

Lecture 8: ARMA – Converting Data into Stationary

Lecture 9: ARMA – ACF & PACF , Train ARMA(p,q)

Lecture 10: ARMA – Evaluation

Lecture 11: ARMA – Visualizing Prediction Results

Lecture 12: ARMA – Convert Stationary to Non – Stationary Data

Lecture 13: ARIMA

Lecture 14: ARIMA : Visualize the output

Chapter 11: Auto ARIMA

Lecture 1: Get best (p,d,q) with Auto ARIMA

Lecture 2: Train ARIMA with Best (p,d,q)

Lecture 3: Forecasting

Chapter 12: Appendix – Python Crash Course

Lecture 1: Install Anaconda Python

Lecture 2: Download the Resources

Lecture 3: Open Jupyter Notebook

Lecture 4: Markdown

Lecture 5: Print Statements