Time Series Forecasting Using Python - eLearning

Course timeline

1. The Concept of Time Series and Its Components

   Lesson 01

   • The Concept and Necessity of Time Series Analysis
   • Granularity, Frequency and Horizon in Time Series Analysis
   • Extracting Data Using Yahoo Finance
   • Time Series Components: Level, Trend, Seasonality, Cyclicality, And Noise
   • Dealing With Missing Value and Outliers in Time Series
   • Additive And Multiplicative Decomposition

2. Dealing with Stationarity

   Lesson 02

   • White Noise
   • Random Walk
   • The Concept of Stationarity
   • Detecting And Handling with Stationarity
   • Statistical Test for Detecting Stationarity: KPSS Vs ADF Test
   • Granger Causality Test
   • Anomaly Detection Using Isolation Forest

3. Stationarity and Lag Identification

   Lesson 03

   • Autocorrelation and Correlation
   • Granger Causality Test
   • Autocorrelation Function (ACF)
   • Partial Autocorrelation Function (PACF)
   • Identification of Lags Using ACF and PACF

4. Basic Time Series Models

   Lesson 04

   • Naive Method
   • Simple Average Method, Moving Average (MA) Model
   • Running Prediction with MA Model
   • Autoregressive Model (AR)
   • Running Prediction with AR Model
   • Holt-winter Exponential Smoothing
   • Single Exponential Smoothing
   • Double Exponential Smoothing

5. Performance Measurement

   Lesson 05

   • Performance Metrics for Time Series Analysis
   • Detecting Performance of the Models
   • Compare The Performance of the Models

6. Advanced Time Series Models

   Lesson 06

   • Autoregressive Moving Average (ARMA) Model
   • Running Prediction with ARMA Model
   • Autoregressive Integrated Moving Average (ARIMA) Model
   • Running Prediction with ARIMA
   • Seasonal Autoregressive Integrated Moving Average (SARIMA) Model
   • Running Prediction with SARIMA

7. Multivariate Time Series Analysis

   Lesson 07

   • The Concept of Endogenous and Exogenous Variables
   • Introduction to SARIMAX: A Brief Theoretical Background
   • Modeling with SARIMAX
   • Running Prediction with SARIMAX
   • Introduction to VAR
   • Modeling with VAR
   • Running Prediction with VAR

8. Time Series Forecasting with Facebook Prophet

   Lesson 08

   • Emergence of Prophet
   • Main Parameters in Prophet
   • Modeling with Prophet
   • Running Prediction with Prophet