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High Performance Time Series
Welcome to High Performance Time Series!
High-Performance Time Series - Become the Time Series Expert for Your Organization (2:34)
Private Slack Channel - How to Join
Video Subtitles (Captions)
What is a High-Performance Forecasting System?
[IMPORTANT] System Requirements - R + Python Requirements & Common Issues
Would You Like To Become An Affiliate (And Earn 20% On Your Sales)?
Prerequisites
Prerequisite - Data Science for Business Part 1
Getting Help
Getting Help (IMPORTANT!!!)
Module 0 - Introduction to High-Performance Forecasting
High-Performance Forecasting - What You're Learning, Why You're Learning It (0:43)
0.1 Forecast Competition Review
The Forecasting Competition Review & Course Progression (3:34)
2014 Kaggle Walmart Recruiting Challenge (5:11)
2018 M4 Competition (3:37)
2018 Kaggle Wikipedia Website Traffic Forecasting Competition (4:30)
2020 M5 Competition (5:59)
5 Key Takeaways from the Forecast Competition Review (5:41)
0.2 Course Projects - Google Analytics, Email Subscribers, & Sales Forecasting
The Business Case - Developing a Best-in-Class Forecasting System (3:03)
0.3 What Tools are in Your Toolbox?
Timetk: Time Series Data Preparation, Visualization, & Preprocessing (5:54)
Modeltime: Time Series Machine Learning (5:25)
GluonTS: Time Series Deep Learning (2:01)
🗺️ [Cheat Sheet] Forecasting Workflow
Module 01 - Time Series Jumpstart
Time Series Jumpstart (0:54)
1.1 Time Series Project Setup
Project Setup (2:28)
🔽 Course Data (File Download) (1:02)
🔽 R Package Installation - Part 1 (File Download) (5:26)
R Package Installation - Part 2 (5:14)
🔽 Jumpstart Setup (File Download) (0:44)
1.2 Business Understanding & Dataset Terminology
Establish Relationships, Part 1 - Google Analytics Summary Dataset (4:11)
Establish Relationships, Part 2 - Google Analytics Top 20 Pages (5:23)
Build Relationships - Mailchimp & Learning Lab Events (4:49)
Generate Course Revenue - Transaction Revenue & Product Events (3:03)
🔽 Code Checkpoint (File Download) (0:54)
1.3 TS Jumpstart: Dive into Forecasting Email Subscribers!
Read This! - Time Series Jumpstart Intent
🔽 Time Series Jumpstart - Setup (File Download) (3:20)
Libraries & Data (3:13)
1.3.1 Exploratory Data Analysis for Time Series
EDA for Time Series (1:08)
Summarize By Time (5:46)
Time Series Summary Diagnostics (4:47)
Pad by Time (4:08)
Visualize the Time Series (3:12)
1.3.2 Evaluation & Train/Test Windows
Evaluation Window - Filter By Time (4:43)
Time Series Train/Test Split (4:53)
1.3.3 Forecasting with Prophet
Training a Prophet Model with Modeltime (4:21)
Modeltime Forecasting Workflow - Round 1 (7:43)
1.3.4 Forecasting with Feature Engineering
Visualizing Seasonality (4:34)
Feature Engineering - Part 1 (5:45)
Feature Engineering - Part 2 (5:51)
Machine Learning with Workflows (3:35)
Modeltime Forecasting Workflow - Round 2 (5:59)
1.3.5 Recap & Code Checkpoint - Module 01 - TS Jumpstart
Here's where you are going. (3:11)
🔽 Code Checkpoint (File Download)
✨[Part 1] Time Series with Timetk
Welcome to Part 1 - Time Series with Timetk! (2:17)
Module 02 - Time Series Visualization
🔽 Setup (File Download) & Overview - Visualization (2:11)
Data Preparation - Part 1 (4:29)
Data Preparation - Part 2 (3:23)
2.1 Time Series Plots [MUST KNOW FUNCTION] 💡
[MUST KNOW] Plotting Time Series 💡 (5:31)
Plotting with Transformations (4:37)
Adjusting the Smoother (6:11)
Smoother for Groups (1:54)
Interactive & Static Plots (2:00)
2.2 Autocorrelation Plots
ACF & PACF Concepts - Autocorrelation & Partial Autocorrelation
ACF & PACF Plotting (7:49)
Lag Adjustment (1:24)
CCF Plotting - Cross Correlations (7:58)
2.3 Seasonality Plots
Seasonality Box Plot (5:52)
Seasonality Violin Plot (0:53)
2.4 Anomaly Plots
Anomaly Plot Basics (4:50)
Getting the Anomaly Data (2:00)
Working with Grouped Data (1:43)
2.5 STL Decomposition & Regression Plots
STL Decomposition Plot (4:44)
STL Decomposition - Grouped Time Series (2:11)
2.6 Regression Plots [SECRET WEAPON FOR FEATURE ENGINEERING]
[SECRET WEAPON] Time Series Regression Plot 💥💥💥 (7:08)
Time Series Regression Plot - Grouped Time Series (4:05)
2.7 Code Checkpoint - Module 02 - Visualization
🔽 Code Checkpoint (File Download)
Module 03 - Time Series Data Wrangling
🔽 Setup (File Download) & Overview - Data Wrangling (2:34)
3.1 Summarise By Time [MUST KNOW] 💡
Single & Grouped Time Series Summarizations (4:37)
Using Across (to Summarize Wide-Format Tibbles by Time) (5:11)
Weekly/Monthly/Quarterly/Yearly Aggregations (3:33)
Floor, Ceiling, Round (5:15)
3.2 Pad by Time
Filling in Gaps (2:54)
From Low-Frequency to High-Frequency (3:36)
3.3 Filter By Time
Zooming & Slicing (5:14)
Offsetting by Time (2:01)
3.4 Mutate By Time
Extrapolate the Mean, Median, Max, Min By Time (7:57)
3.5 Joining By Time
Combining Subscribers & Web Traffic (3:48)
Inspecting the Join (3:00)
Formatting the Join for Feature Relationships (5:49)
Join Cross Correlations (3:22)
3.6 Time Series Index Operations
Making a Time Series (4:39)
Making a Holiday Sequence (3:14)
Time Offsets (3:01)
Making a Future Time Series (3:12)
3.7 Forecasting with Future Frames 📈
The Future Frame (2:47)
[FORECAST SPOTLIGHT] Forecasting with the Future Frame 📈 (6:53)
3.8 Code Checkpoint - Module 03 - Data Wrangling
🔽 Code Checkpoint (File Download)
Module 04 - Transformations for Time Series
🔽 Setup (File Download) & Overview - Transformations (2:15)
Libraries & Data (2:12)
4.1 Variance Reduction Transformations - Log & Box Cox [MUST KNOW] 💡
Why is Variance Reduction Important? (4:43)
Log - Log (and Log1P) Transformation (4:17)
Log - Assessing the Benefit of Log1P Transformation (2:51)
Log - Groups & Inversion (3:43)
Box Cox - What is the Box Cox Transformation? (2:34)
Box Cox - Assessing the Benefit (4:04)
Box Cox - Inversion (2:05)
Box Cox - Managing Grouped Transformations & Inversion (8:36)
4.2 Rolling & Smoothing Transformations
Introduction to Rolling & Smoothing (1:49)
🔽 Rolling Windows - What is a Moving Average? (File Download) (3:53)
Rolling Windows - Moving Average & Median Applied (8:53)
Loess Smoother (7:02)
Rolling Correlation - Slidify, Part 1 (4:16)
Rolling Correlation - Slidify, Part 2 (7:40)
[BUSINESS SPOTLIGHT] The Problem with Forecasting using a Moving Average (6:43)
4.3 Range Reduction Transformations
Introduction to Normalization & Standardization (0:59)
What is Normalization? [Min = 0, Max = 1] (4:50)
What is Standardization? [Mean = 0, Standard Deviation = 1] (2:31)
4.4 Imputation & Outlier Cleaning
Introduction to Imputation & Outlier Cleaning (0:44)
Imputation - Time Series NA Repair (6:40)
Anomalies - Time Series Outlier Cleaning (7:22)
Anomalies - When to Remove Outliers (5:21)
4.5 Lags & Differencing Transformations [MUST KNOW] 💡
Introduction to Lags & Differencing (1:08)
Lags - What is a Lag? (1:49)
Lags - Lag Detection with ACF/PACF (3:54)
Lags - Regression with Lags (5:06)
Differencing - Growth vs Change (4:00)
Differencing - Acceleration (6:22)
Differencing - Comparing Multiple Time Series (4:44)
Differencing - Inversion (0:57)
4.6 Fourier Series [MUST KNOW] 💡
Introduction to the Fourier Series (7:23)
Fourier Regression (4:24)
4.7 Constrained Interval Forecasting [FORECAST SPOTLIGHT] 📈
What is the Log Interval Transformation? (5:47)
Visualizing the Transformation (4:12)
Transformations & Preprocessing (5:09)
Modeling (6:29)
Preparing Future Data (3:36)
Making Predictions (1:05)
Combining the Forecast Data (4:08)
Estimating Confidence Intervals (8:24)
Visualizing Confidence Intervals (2:10)
Inverting the Log Interval Transformation (4:08)
4.8 Code Checkpoint - Module 04 - Transformations
🔽 Code Checkpoint (File Download)
⛰️ Challenge #1 - Exploring Transactions & Web Page Traffic
🔽 Challenge #1 Discussion (File Download) (4:21)
🔽 Solution - Part 1 (File Download) (7:18)
Solution - Part 2: Begins at "Identify Relationships" (7:51)
Module 05 - Introduction to Feature Engineering (for Time Series)
🔽 Setup (File Download) & Overview - Intro to Feature Engineering (2:30)
Data Prep, Part 1 - Log Standardize (5:27)
Data Prep, Part 2 - Getting Ready to Clean (5:01)
Data Prep, Part 3 - Targeted Cleaning with Between Time (4:18)
5.1 Time-Based Features (Trend & Seasonal/Calendar) [MUST KNOW] 💡
The Time Series Signature (7:55)
Feature Removal (3:28)
Linear Trend (2:10)
Non-Linear Trend - Basis Splines (4:41)
Non-Linear Trend - Natural Splines (Stiffer than Basis Splines) (4:29)
Seasonal Features - Weekday & Month (3:21)
Seasonal Features - Combining with Trend (5:23)
5.2 Interactions
Interaction Features - Spikes Every Other Wednesday (7:35)
5.3 Fourier Features
Selecting & Adding Fourier Frequency Features (4:21)
Modeling & Visualizing the Fourier Effects (2:07)
5.4 Autocorrelated Lag Features
Selecting & Adding Lag Features (6:59)
Modeling & Visualizing the Lag Effects (5:20)
5.5 Special Event Features
Preparing Event Data for Analysis (6:34)
Visualizing Events (2:57)
Modeling & Visualizing Event Effects (2:08)
Fixing the Spline (2:07)
5.6 External Regressors (Xregs)
Transforming Xregs (5:05)
Joining Xregs (1:49)
Examining Cross Correlations (1:53)
Modeling with Xregs (3:28)
Visualizing PageViews vs Optins & Modeling Lags (6:58)
5.7 Recommended Model Features
Collecting the Recommended Model (3:44)
Saving the Model Artifact (2:28)
5.8 Code Checkpoint - Module 05 - Introduction to Feature Engineering
🔽 Code Checkpoint (File Download)
Module 06 - Advanced Feature Engineering Workflow
Forecasting Workflow [CHEAT SHEET] 🗺️ (3:40)
🔽 Setup (File Download) & Overview - Advanced Feature Engineering (1:43)
Data Preparation (4:42)
6.1 Creating the "Full" Dataset - Extending & Adding Lagged Features & Events [IMPORTANT] 💡
The "Full" Dataset (2:50)
Extending - Future Frame (3:21)
Adding Lag Features (4:02)
Add Lagged Rolling Features (5:03)
Add Events (External Regressors) (2:57)
Format Column Names (3:09)
6.2 Separate into Modeling Data & Forecast Data
Data Prepared / Future Data Split (2:48)
6.3 Separate into Training Data & Testing Data
Train / Test Split (3:55)
6.4 Recipes - Feature Engineering Pipeline Steps
Recipes Intro (2:41)
Step - Time Series Signature Features (5:48)
Step - Feature Removal (3:10)
Step - Standardization (2:11)
Step - One-Hot Encoding (1:55)
Step - Interaction Features (2:28)
Step - Fourier Series Features (2:03)
6.5 Building the Spline Model
Model Spec: LM Model (1:02)
Recipe Spec: Spline Features (5:59)
Workflow: Spline Recipe + LM Model (2:49)
6.6 Introduction to Modeltime Workflow
Modeltime Table & Calibration (2:08)
Forecasting the Test Data (2:40)
Measuring the Test Accuracy (1:19)
Comparing the Training & Testing Accuracy (1:32)
6.7 Building the Lag Model
Recipe Spec: Lag Features (3:00)
Workflow: Lag Recipe+ LM Model (2:40)
Modeltime: Comparing Spline & Lag Models (4:23)
6.8 Forecasting the Future
Refitting the Models (4:37)
Transformation Inversion (5:23)
Visualizing the Forecast in the Original Scale (1:59)
Overfitting (An Optional Fix)
6.9 Saving the Artifacts
Creating an Artifact List, Part 1 (4:34)
Creating an Artifact List, Part 2 (3:11)
Organizing the Artifacts List (1:57)
Saving the Artifacts (1:28)
6.10 Code Checkpoint - Module 06 - Advanced Feature Engineering
🔽 Code Checkpoint (File Download)
⛰️ Challenge #2 - Feature Engineering & Modeltime Workflow [YOU'VE GOT THIS!]
🔽 Challenge Discussion, Part 1 (File Download) - Feature Preparation (5:11)
Challenge Discussion, Part 2 - Feature Engineering & Modeling (4:56)
Challenge #2 - Solution
🔽 Solution, Part 1 (File Download) - Collect & Prepare Data (3:49)
Solution, Part 2 - Visualizations (3:19)
Solution, Part 3A - Create Full Dataset (5:46)
Solution, Part 3B - Visualize the Full Dataset (3:47)
Solution, Part 4 - Model/Forecast Data Split (1:05)
Solution, Part 5 - Train/Test Data Split (0:56)
Solution, Part 6 - Feature Engineering (4:18)
Solution, Part 7 - Modeling: Spline Model (6:08)
Solution, Part 8 - Modeling: Lag Model (2:25)
Solution, Part 9 - Modeltime (4:03)
Solution, Part 10 - Forecast (6:49)
Challenge #2 Bonus - Regularization
🔽 Regularization, Part 1 (File Download) - Model: GLMnet (4:01)
Regularization, Part 2 - Improving the Lag Model with GLMNet (5:28)
Regularization, Part 3 - Forecasting the Future Data with GLMNet + Lag Recipe (3:02)
Part 1 Complete - You rock! 🙌🙌🙌
WOOO HOOO - You crushed it!
✨[Part 2] Machine Learning for Time Series with Modeltime
🔽 Picking Up From Part 1 (Project Download)
Module 07 - Modeltime Workflow [DEEP DIVE] 🌊
Setup - Modeltime Workflow [In-Depth] (1:25)
Overview - Modeltime Workflow [In-Depth] (1:16)
Libraries & Artifacts Preparation (2:33)
7.1 Making Models - Object Types & Requirements
Model Requirements for Modeltime (1:34)
Parsnip Object Models - Univariate (3:37)
Workflow Objects - Multivariate, Date-Based Features (7:14)
Workflow Object - Multivariate, External Features (4:53)
7.2 Modeltime Table
Modeltime Table - Key Requirements (4:27)
7.3 Calibration Table
Calibration Table - How It Works (3:29)
7.4 Measuring Model Accuracy [IMPORTANT!!!]
Primary Accuracy Metrics & Uses [SUPER IMPORTANT] (7:40)
Custom Metric Sets using Yardstick (3:54)
Customizing the Accuracy Table Output (3:28)
7.5 Forecasting the Test Data
Modeltime Forecast - How It Works (6:22)
Customizing the Forecast Visualization (5:00)
7.6 Model Refitting & Forecasting
Refitting - How It Works (3:02)
Making the Forecast (5:20)
7.7 Code Checkpoint - Module 07A - Modeltime Workflow [In-Depth]
🔽 Code Checkpoint (File Download)
7.8 New Features of Modeltime 0.1.0 - Module 07B 🆕
🔽 Setup (File Download) - Modeltime New Features (1:53)
Expedited Forecasting - Modeltime Table (5:20)
Expedited Forecasting - Skip Straight to Forecasting (2:20)
Visualizing a Fitted Model (2:57)
Calibration - In-Sample vs Out-of-Sample Accuracy (5:25)
Residual Diagnostics - Getting Residuals (2:16)
Residuals - Time Plot (2:39)
Residuals - Plot Customization (2:29)
Residuals - ACF Plot (4:06)
Residuals - Seasonality Plot (3:50)
7.9 Code Checkpoint - Module 07B - Modeltime New Features!
🔽 Code Checkpoint (File Download)
Module 08 - ARIMA
🔽 Setup (File Download) (0:40)
ARIMA Training Overview (1:29)
Libraries & Artifacts Setup (1:49)
8.1 ARIMA Concepts 💡
Auto-Regressive Functions: ar() & arima() (5:15)
Auto-Regressive (AR) Modeling with Linear Regression (3:11)
Single-Step Forecast for AR Models (4:43)
Multi-Step Recursive Forecasting for AR Models (4:44)
Integration (Differencing) (5:42)
Moving Average (MA) Process (Error Modeling) (7:36)
Seasonal ARIMA (SARIMA) (4:29)
Adding XREGS (SARIMAX) (4:44)
8.2 ARIMA in Modeltime
Setting Up Basic ARIMA in Modeltime (4:45)
Trying Different ARIMA Parameters (5:11)
About AIC (Akaike Information Criterion) (3:42)
8.3 Modeltime Auto ARIMA
Implementing Auto ARIMA in Modeltime (1:49)
How Auto ARIMA Works - Lazy Grid Search (1:27)
Comparing ARIMA & Auto ARIMA (3:15)
Adding Fourier Features to Pick Up More than 1 Seasonality (3:49)
Adding Event Features to Improve R-Squared (Variance Explained) (1:33)
Refitting & Reviewing the Forecast (2:57)
Adding Month Features to Account for February Increase - BEST MAE 0.564 (3:35)
8.4 Recap - ARIMA
ARIMA Strengths & Weaknesses (and Strategies that Worked) (3:56)
Saving Artifacts - Best ARIMA Model (3:28)
8.5 Code Checkpoint - Module 08 - ARIMA
🔽 Code Checkpoint (File Download)
Module 09 - Prophet
🔽 Setup (File Download) (0:27)
Prophet Training Overview (0:51)
Libraries & Artifacts (2:02)
9.1 Prophet with Modeltime
Prophet Regression: prophet_reg() (3:23)
Modeltime Workflow (2:02)
Adjusting the Key Prophet Parameters (5:13)
9.2 Prophet Concepts 💡
Extracting the Prophet Model from Modeltime (3:11)
Visualizing the Effect of Key Parameters on the Prophet Model (5:48)
Understanding Prophet Components & Additive Model (2:37)
9.3 Back to Modeling with Prophet - XREGS!
Fitting Prophet w/ Events (2:19)
Comparing No Events vs Events - BEST MAE 0.488 (w/ Events) 🚀 (3:05)
Making the Forecast (2:10)
9.4 Recap - Prophet
Logging (Saving) Your Progress (2:40)
Recap - Prophet Strengths & Weaknesses (3:02)
9.5 Checkpoint - Module 09 - Prophet
🔽 Code Checkpoint (File Download)
Module 10 - Exponential Smoothing, TBATS, & Seasonal Decomposition
🔽 Setup (File Download) (0:18)
Overview - Exponential Smoothing (0:35)
Libraries & Artifacts (1:37)
10.1 Exponential Smoothing
The Exponential Weighting Function (4:50)
Applying the Exponential Weighting Function to Make a Forecast (2:41)
ETS Model: exp_smoothing() (3:52)
Visualizing the ETS Model (4