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Time Series Forecasting with Python
Introduction
Byte Sized Learning
Exercise: Meet Your Classmates and Instructor
Understanding Your Video Player (notes, video speed, subtitles + more)
Set Your Learning Streak Goal
Section 1: The Anatomy of a Forecasting Product
Course Introduction (5:03)
Course Material (2:12)
Why Forecasting Matters (5:15)
Let's Have Some Fun (+ Free Resources)
Section 2: Exploratory Data Analysis
Game Plan (1:04)
TIme Series Data (2:34)
Case Study Briefing (1:43)
Python - Directory and Libraries (4:42)
Python - Loading the Data (2:51)
Python - Renaming Variable (1:29)
Python - Summary Statistics (2:33)
Additive vs. Multiplicative Seasonality (2:40)
Python - Seasonal Decomposition (8:18)
Python - Seasonal Graphs (5:07)
Python - Visualization - Basic Plot (5:31)
Python - Visualization - Customization (5:58)
Python - Visualization -Adding Events (6:10)
Python - Correlation (2:18)
Auto-Correlation Plots (2:01)
Python - Auto-Correlation Plot (3:20)
Python - Useful Commands Template (2:30)
Unlimited Updates
Section 3: Facebook Prophet
Facebook Prophet Game Plan (1:36)
Structural Time Series and Facebook Prophet (4:42)
Python - Preparing the Script (3:50)
Python - Date Variable (2:04)
Python - Easter (4:08)
Python - Thanksgiving (1:24)
Python - Wrapping Up the Events (2:32)
Facebook Prophet Parameters (2:04)
Facebook Prophet Model (3:49)
Cross-Validation (2:24)
Python - Cross-Validation (5:29)
Assessing Model Errors (4:37)
Python - Cross-Validation Performance and Plot (7:52)
Parameter Tuning (1:50)
Python - Parameter Grid (4:47)
Python - Parameter Tuning (6:56)
Python - Best Parameters and Exporting (6:55)
Python - Building Script (5:06)
Python - Preparing Data Sets (5:54)
Python - Final Facebook Prophet Model (7:04)
Python - Forecasting (7:36)
Python - Exporting Forecast (5:15)
Facebook Prophet Pros and Cons (1:40)
Course Check-In
Section 4: SARIMAX
SARIMAX Game Plan (1:52)
ARIMA (3:05)
Python - Preparing Script (3:29)
Auto-Regressive (1:54)
Integrated (4:33)
Python - Stationarity and Differencing (5:44)
Moving Average Component (2:34)
Optimization Factors (3:15)
Python - SARIMAX Model (5:11)
Python - Cross-Validation (8:18)
Python - Parameter Grid (3:32)
Python - Parameter Tuning (4:14)
Python - Exporting Best Parameters (4:26)
Python - Preparing the Script (3:27)
Python - Preparing Data (2:56)
Python - Tuned SARIMAX Model (4:02)
Python - Forecasting (4:28)
Python - Visualization and Export (3:54)
SARIMAX Pros and Cons (1:48)
Implement a New Life System
Section 5: How LinkedIn Silverkite Works
LinkedIn Silverkite Game Plan (1:35)
LinkedIn Silverkite (3:08)
Silverkite vs. Prophet (3:11)
Python - Libraries and Data (10:05)
Python - Preparing Data (3:36)
Python - Metadata (2:47)
Silverkite Components (4:20)
Growth Terms (1:56)
Python - Growth Terms (2:04)
Seasonality Terms (3:20)
Python - Seasonality (2:06)
Python - Available Countries and Holidays (3:31)
Python - Holidays (6:21)
Python - Changepoints (1:22)
Python - Regressors (1:15)
Lagged Regressors (1:54)
Python - Lagged Regressors (1:37)
Python - Autoregression (2:19)
Fitting Algorithms Possibilities (2:38)
Ridge Regression (7:52)
XGBoost (3:44)
Boosting (7:03)
Feature Sampling (2:56)
Python - Custom Fit Algorithm (2:12)
Python - Silverkite Model (2:48)
Python - Cross-Validation Configuration (8:27)
Python - SIlverkite Parameter Tuning (6:01)
Python - Visualization and Preparing Results (8:01)
Python - Exporting Best Parameters (6:06)
Python - Preparing Script (3:33)
Python - Best Parameters and Silverkite Model (7:51)
Python - Summary and Visualization (6:26)
Python - Exporting Forecasts (3:06)
Pros and Cons (2:09)
Section 6: Recurrent Neural Networks (RNN) Long Short-Term Memory (LSTM)
Recurrent Neural Networks (RNN) Long Short-Term Memory (LSTM) Game Plan (2:13)
Simple Neural Network (5:58)
Recurrent Neural Networks (RNN) (3:27)
Long Short-Term Memory (LSTM) (5:31)
Python - Libraries and Data (5:20)
Python - Time Series Objects (4:55)
Python - Time Variables (9:01)
Python - Scaling Variables (9:03)
LSTM Parameters (2:14)
Python - LSTM Model (8:57)
Python - Cross-Validation (4:22)
Python - CV Performance (10:21)
Python - Parameter Grid (4:21)
Python - Parameter Tuning (Round 1) (7:18)
Python - Parameter Tuning (Round 2) (6:42)
Python - Parameter Tuning (Final Results) (2:28)
Python - Preparing Script (3:59)
Python - Preparing Inputs (3:47)
Python - Tuned LSTM Model (4:17)
Python - Predictions and Exporting (4:04)
LSTM Pros and Cons (2:20)
Section 7: Ensemble
Ensemble Game Plan (1:21)
Ensemble Mechanism (4:43)
Python - Preparing Script and Loading Predictions (7:23)
Python - Loading Errors (5:02)
Python - Forecasting Weights (4:55)
Python - Ensemble Forecast and Visualization (3:31)
Ensemble Pros and Cons (2:17)
Where To Go From Here?
Thank You! (1:17)
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