Name: High Performance Time Series
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SKU: HPTS
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High Performance Time Series

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SKU: HPTS Category: Business Marketing Tag: High Performance Time Series

High Performance Time Series

The High-Performance Time Series Forecasting Course is an amazing course designed to teach Business Analysts and Data Scientists how to reduce forecast error using state-of-the-art forecasting techniques that have won competitions. You’ll undergo a complete transformation learning the most in-demand skills that organizations need right now. Time to accelerate your career.

Crafted For Business Analysts & Data Scientists:

That need to reduce forecasting error and scale results for your organization.

This is possibly my most challenging course ever. You’ll learn the time series skills that have taken me 10-years of study, practice, and experimentation.

My talk on High-Performance Time Series Forecasting

This course gives you the tools you need to meet today’s forecasting demands.

A full year was spent on building two of the software packages you’ll learn, modeltime and timetk.

Plus, I’m teaching you GluonTS, a state-of-the-art deep learning framework for time series written in python.

This course will challenge you. It will change you. It did me.

– Matt Dancho, Course Instructor & Founder of Business Science

Undergo a Complete Transformation
By learning forecasting techniques that get results

With High-Performance Forecasting, you will undergo a complete transformation by learning the most in-demand skills for creating high-accuracy forecasts.

Through this course, you will learn and apply:

– Machine Learning & Deep Learning

– Feature Engineering

– Visualization & Data Wrangling

– Transformations

– Hyper Parameter Tuning

– Forecasting at Scale (Time Series Groups)

How it works:

Your path to becoming an Expert Forecaster is simplified into 3 streamlined steps.

Time Series Feature Engineering
Machine Learning for Time Series
Deep Learning for Time Series

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Part 1

Time Series Feature Engineering:

First, we build your time series feature engineering skills. You learn:

– Visualization: Identifying features visually using the most effective plotting techniques

– Data Wrangling: Aggregating, padding, cleaning, and extending time series data

– Transformations: Rolling, Lagging, Differencing, Creating Fourier Series, and more

– Feature Engineering: Over 3-hours of content on introductory and advanced feature engineering

Part 2

Machine Learning for Time Series:

Next, we build your time series machine learning skills. You learn:

– 17 Algorithms: 8 hours of content on 17 TOP Algorithms. Divided into 5 groups:

– ARIMA

– Prophet

– Exponential Smoothing – ETS, TBATS, Seasonal Decomposition

– Machine Learning – Elastic Net, MARS, SVM, KNN, Random Forest, XGBOOST, Cubist, NNET & NNETAR

– Boosted Algorithms – Prophet Boost & ARIMA Boost

– Hyper Parameter Tuning: Strategies to reduce overfitting & increase model performance

– Time Series Groups: Scale your analysis from one time series to hundreds

– Parallel Processing: Needed to speed up hyper parameter tuning and forecasting at scale

– Ensembling: Combining many algorithms into a single super learner

Part 3

Deep Learning for Time Series:

Next, we build your time series deep learning skills. You learn:

GluonTS: A state-of-the-art forecasting package that’s built on top of mxnet (made by Amazon)
Algorithms: Learn DeepAR, DeepVAR, NBEATS, and more!

Challenges & Cheat Sheets

Next, we build your time series machine learning skills. You learn:

– Cheat Sheets: Developed to make your forecasting workflow reproducible on any problem

– Challenges: Designed to test your abilities & solidify your knowledge

Summary of what you get

– A methodical training plan that goes from concept to production ($10,000 value)

– Part 1 – Feature Engineering with Timetk

– Part 2 – Machine Learning with Modeltime

– Part 3 – Deep Learning with GluonTS

– Challenges & Cheat Sheets

Your Instructor:

Matt Dancho

Founder of Business Science and general business & finance guru, He has worked with many clients from Fortune 500 to high-octane startups! Matt loves educating data scientists on how to apply powerful tools within their organization to yield ROI. Matt doesn’t rest until he gets results (literally, he doesn’t sleep so don’t be suprised if he responds to your email at 4AM)!

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Course Curriculum

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

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 Jump

Time Series Jump (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)
Jump 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 Jump: Dive into Forecasting Email Subscribers!

Read This! – Time Series Jump Intent
Time Series Jump – 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 Jump

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)

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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)

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)

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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 (4:48)

10.2 TBATS

TBATS Model: seasonal_reg() (3:36)
Visualizing the TBATS Model (2:48)

10.3 Seasonal Decomposition Models

Seasonal Decomposition & Multiple Seasonality Time Series (MSTS) Objects (2:28)
STLM ETS Model (2:33)
STL Plot & Relationship to STLM ETS Model (2:49)
STLM ARIMA Model (1:55)
STLM ARIMA – Adding XREGS (1:08)

10.4 Evaluation

Preparing the Test Forecast Visualization (3:30)
Comparing Multiple Models – ETS, TBATS, STLM ARIMA & ETS – BEST MAE 0.523 (TBATS) (3:45)
Refitting – Examining the Future Forecasts (3:34)

10.5 Recap – ETS, TBATS, Seasonal Decomp

Saving Artifacts (2:22)
Strengths & Weaknesses – ETS, TBATS, Seasonal Decomp (2:05)

10.6 Code Checkpoint – Module 10 – ETS, TBATS, & Seasonal Decomposition

Code Checkpoint (File Download)

Challenge #3 – ARIMA + Prophet + ETS + TBATS

Challenge #3 Discussion, Part 1 (File Download) – through ARIMA (5:32)
Challenge #3 Discussion, Part 2 – Prophet to End of Challenge (2:33)

Challenge #3 – Solution

Solution, Part 1 – Train/Test Setup (Solution File Download) (1:55)
Solution, Part 2 – ARIMA (Model 1): Basic Auto ARIMA (3:03)
Solution, Part 3 – ARIMA (Model 2): Auto ARIMA + Adding Product Events (2:14)
Solution, Part 4 – ARIMA (Model 3): Auto ARIMA + Events + Seasonality (2:08)
Solution, Part 5 – ARIMA (Model 4): Forcing Seasonality with Manual ARIMA (1:17)
Solution, Part 6 – ARIMA (Model 5): Auto ARIMA + Events + Fourier Series (0:57)
Solution, Part 7 – ARIMA – Modeltime Workflow (2:26)
Solution, Part 8 – ARIMA – Forecast Review (3:18)
Solution, Part 9 – Prophet Models: Basic (6), Yearly Seasonality (7), Events (8), Events + Fourier (9) (2:52)
Solution, Part 10 – Prophet – Modeltime Workflow (1:38)
Solution, Part 11 – Prophet – Forecast Review (3:13)
Solution, Part 12 – Exponential Smoothing Models: ETS (10), TBATS (11) (3:24)
Solution, Part 13 – Exponential Smoothing – Modeltime Workflow (1:45)
Solution, Part 14 – Exponential Smoothing – Forecast Review (1:30)
Solution, Part 15 – Forecasting the Future Data – ARIMA, Prophet & ETS/TBATS (3:40)
Solution, Part 16 – Final Review – ARIMA, Prophet, & ETS/TBATS (2:47)

Challenge #3 BONUS – ARIMA & Prophet vs Linear Model

Bonus, Part 1 (File Download) – Adding the LM from Challenge #2 (4:43)
Bonus, Part 2 – Why is the LM forecast high in March? (4:41)

11.0 Machine Learning Algorithms [IMPORTANT]

Welcome to Machine Learning for Time Series (File Download) (5:22)

11.1 Elastic Net Algorithm (GLMNet) – Linear

GLMNet – Model Spec (3:43)
GLMNet – Spline & Lag Workflows (2:40)
GLMNet – Calibration, Accuracy, & Plot (4:06)
GLMNet – Tweaking Parameters – BEST MAE 0.519 (Lag Model) (2:33)

Plotting Utility – Let’s make a helper function to speed evaluation up!

calibrate_and_plot() (5:50)
Visualizing the Effect of Parameter Adjustments (3:19)

11.2 Multiple Adaptive Regression Splines (MARS) – Linear

We come from MARS (3:30)
MARS – A Simple Example (6:55)
MARS – Spline & Lag Models – BEST MAE 0.518 (Spline Model) (4:28)

11.3 Support Vector Machine (SVM) – Polynomial

SVM Polynomial – Model Specification (2:54)
SVM Poly – Tweaking Parameters – BEST MAE 0.615 (Spline Model) – BOOO (5:09)

11.4 Support Vector Machine (SMV) – Radial Basis Function

16% Improvement – SVM RBF vs SVM Poly (2:29)
SVM RBF – Parameter Tweaking (3:11)
SVM RBF – Lag Model – BEST MAE 0.520 (Spline Model) – Niiiice! (1:55)

11.5 [Important Concept] KNN & Tree-Based Algorithms – The Problem with Predicting Time Series Trends

Strengths/Weakness – KNN & Tree-Based Algorithms Can’t Predict Beyond the Min/Max (1:24)
KNN vs GLMNET – Making Sample Data with Trend (2:08)
KNN vs GLMNET – Making Simple Trend Models (4:12)
KNN vs GLMNET – Visualize the Trend Predictions w/ Modeltime – Yikes, GLMNET just schooled KNN (4:14)

11.5 K-Nearest Neighbors (KNN) – Similarity (Distance) Based

KNN – Spline Model (3:30)
KNN – Tweaking Key Parameters (5:52)
KNN – Lag Model – BEST MAE 0.558 (Spline Model) (2:05)

You’re kicking butt… But, don’t forget to take breaks

[COFFEE BREAK] With Bill Murray

11.6 Random Forest (Tree-Based)

RF – Spline Model (4:27)
RF – Lag Model – 32% Better vs Spline Model (3:11)
RF – Tweaking Parameters – BEST MAE 0.516 (Lag Model) (4:02)

11.7 XGBoost (Gradient Boosting Machine) – Tree-Based

XGBoost – Spline & Lag Models (5:00)
XGBoost – Tweaking Parameters – 0.484 MAE (Lag Model) (6:35)
XGBoost – Tweaking Parameters 2 – BEST MAE 0.484 (Lag Model) 🚀 (3:32)

11.8 Cubist – Combo of Trees (Rules) + Linear Models at Nodes

Cubist – Spline & Lag Models – 0.514 MAE out of the gate! (4:53)
Cubist – Tweaking Parameters – OPTIMAL MAE / R-SQUARED (0.524 / 0.316) (5:48)

11.9 Neural Net (NNET) – Like a Linear Regression but Better

NNET – Spline & Lag Models (4:57)
NNET – Tweaking Parameters – BEST MAE 0.553 (Spline Model) (5:39)

11.10 NNETAR – Combining AR Terms with a NNET!

What the heck is NNETAR? (NNET + ARIMA – IMA = NNETAR) (2:22)
NNETAR – Model, Recipe, & Workflow (4:11)
NNETAR – Tweaking AR Parameters (2:24)
NNETAR – Tweaking NNET Parameters – BEST MAE 0.512 (4:13)

11.11 Modeltime Experimentation Review

Organizing in a Modeltime Table (4:22)
Updating the Descriptions Programmatically (4:02)
Model Selection – Process & Tips (using Accuracy Table) (3:39)
Model Inspection – Process & Tips (using Test Forecast Visualization) (3:03)
Model Inspection – Visualizing the Future Forecast (5:42)

11.12 Saving Your Work – Artifacts!

Saving Models (2:34)
Saving your calibrate_and_plot() function (1:29)

11.13 Checkpoint – Module 11 – Machine Learning Algorithms

Code Checkpoint (File Download)

12.0 Boosted Algorithms – Prophet Boost & ARIMA Boost

Boosted Algorithms – A Powerful Technique for Improving Performance (3:37)

12.1A Prophet Baseline Model

Baseline: Best Prophet Model (2:38)
[Pro Tip] How to Fix a Broken Model (2:50)
Prophet Baseline – Best Model MAE 0.488 (0:54)

12.1B Prophet Boost

Recipe for Prophet Boost (3:33)
Model Strategy – Using XGBOOST for Seasonality/XREG Modeling (4:39)
Workflow – No Parameter Tweaking (3:41)
[KEY CONCEPT] Prophet Boost – Modeling Trend with Prophet, Residuals with XGBoost (3:00)
Prophet Boost – Tweaking Parameters – BEST MAE 0.457 🚀 (6:33)

12.2 ARIMA Boost

Modeling Strategy – ARIMA for trend, XGBOOST for XREGS (3:50)
ARIMA Boost – Model Specification (5:57)
ARIMA Boost – Tweaking Parameters – BEST MAE 0.523 (4:34)

12.3 Boosted Models – Modeltime Workflow

Modeltime – Accuracy Evaluation & Identifying Broken Models (2:43)
Modeltime – Forecast Test Data (2:10)
Modeltime – Refitting & Forecasting Future (3:08)
Save Your Work (1:26)

12.4 Code Checkpoint – Boosted Algorithms

Code Checkpoint (File Download)

13.0 Hyper Parameter Tuning & Cross Validation – For Time Series

Hyperparameter Tuning for Time Series (File Downloads) (3:56)
[CHEAT SHEET] Hyperparameter Tuning Workflow (4:47)
Getting ed – Setup & Workflow (3:09)

13.1 Reviewing 28 Models (It’s Easy with Modeltime)

Combining Our Artifacts – 28 Models!(3:06)
Accuracy Review & Hyperparameter Tuning Candidate Selection (This Used to Take Me Weeks To Do) (4:36)

13.2 [SEQUENTIAL MODELS] NNETAR – Hyperparameter Tuning Process

What are Sequential Models? (& Why do we need to tune them differently?) (2:55)
Extracting the Workflow from a Modeltime Table: pluck_modeltime_model() (1:40)
Time Series Cross Validation (TSCV) Specification, Part 1: time_series_cv() (4:34)
Time Series Cross Validation (TSCV), Part 2: plot_time_series_cv_plan() (4:14)
Identify Tuning Parameters – Recipe Spec (3:07)
Identify Tuning Parameters – Model Spec (5:14)
Make a Grid for Parameters – Grid Spec (5:55)

13.2.1 – NNETAR Tuning, Round 1 – Default Params

Grid Latin Hypercube Specification: grid_latin_hypercube() (3:19)
Tuning Workflow Preparation (3:30)
Tune Grid & Show Results (7:24)
Visualize the Parameter Results (3:24)

13.2.2 NNETAR Tuning, Round 2 – Finding the Sweet Spot!

Update Grid Parameter Ranges (8:13)
Parallel Processing – Speed-Up Tuning (5:13)
Speed Comparison (Parallel vs Series) – 3.4X Speed Boost (44 sec vs 151 sec)
Review Parameters vs Performance Metrics (1:09)
NNETAR – Train the Final Model – Best RMSE 0.507(4:15)

13.3 [NON-SEQUENTIAL MODELS] Prophet Boost – Hyperparameter Tuning Process

What are Non-Sequential Models? (2:44)
Model Extraction: pluck_modeltime_model() (1:04)
K-Fold Cross Validation (Use with Non-Sequential Models ONLY) (4:23)
Prophet Boost – Recipe (1:10)
Prophet Boost – Model Spec (Identify Parameters for Tuning) (3:57)

13.3.1 Prophet Boost Tuning, Round 1 – Default Parameters

Grid Specification – Grid Latin Hypercube w/ Default Parameters (4:52)
Tuning the Grid (in Parallel) (6:18)
Visualize Results – Learning Rate Dominates (2:58)

13.3.2 Prophet Boost Tuning, Round 2 – Controlling Learning Rate

Grid Specification – Controlling Learning Rate (4:45)
Hyperparameter Tuning – Round 2 – We can see parameter trends!(3:17)

13.3.3 Prophet Boost Tuning, Round 3 – Honing In

Grid Specification & Tuning – Honing the parameter ranges in (5:49)
Best RMSE Model (Central Tendency) – MAE 0.466, RMSE 0.630, RSQ 0.450 (6:13)
Best R-Squared Model (Variance Explained) – MAE 0.464, RMSE 0.643, RSQ 0.459 (2:42)

13.4 Saving Our Progress

Recap & Saving the Models (6:53)

13.5 Code Checkpoint – Model 13 – Hyperparameter Tuning

Code Checkpoint (File Download)

14.0 Ensemble Time Series Models (Stacking)

Competition Ensembling Review (5:57)
What is an Ensemble Model? (7:21)
Modeltime Ensemble: Documentation (2:01)
Forecasting Cheat Sheet Upgrade [Download Here] (1:00)

14.1 Model Performance Review

Code Setup [File Download] (6:49)
Reviewing Models – Combining Tables & Organizing Results (4:24)
Reviewing Models – Making Sub-Model Selections (7:46)

14.2 Average Ensemble

Mean Ensemble – RMSE 0.640 vs 0.630 (Best Submodel) (5:00)
Median Ensemble – RMSE 0.648 vs 0.630 (Best Submodel) (2:23)

14.3 Weighted Average Ensembles

Introduction to Weighted Ensembles (1:02)
Loading Selection (4:29)
Accuracy Assessment – RMSE 0.628 vs RMSE 0.630 (Baseline) (2:37)

14.4.A Stacked Ensembles – Stacking Process

Introduction to Meta-Learner Ensembling with Modeltime Ensemble (3:57)
Resampling: Time Series Cross Validation (TSCV) Strategy (5:17)
Making Sub-Model CV Predictions – modeltime_fit_resamples() (4:27)
Resampling & Sub-Model Prediction: K-Fold Strategy (6:28)
Linear Regression Stack – TSCV – RMSE 1.00 (Ouch!) (7:16)
Linear Regression Stack – K-Fold – RMSE 0.651 (Much Better, but We Can Do Better)(3:25)

14.4.B Stacked Ensembles – Stacking with Tunable Algorithms

GLMNET Stack – RMSE 0.641 (On the right track) (6:38)
Modeltime Ensemble – In-Sample Prediction Error – Bug Squashed (1:10)
Random Forest Stack – RMSE 0.587!!! (7% improvement) (4:33)
Neural Net Stack – RMSE 0.643 (4:05)
XGBoost Stack – RMSE 0.585!!! (4:29)
Cubist Stack – RMSE 0.649 (3:11)
SVM Stack – RMSE 0.608!! (3:26)

14.5 Multi-Level Stacking

Level 2 – Model Evaluation & Selection (4:27)
Level 3 – Weighted Ensemble Creation, Evaluation, & Selection – RMSE 0.595 (Level 2 RF is New Baseline RMSE 0.585) (3:34)

14.6 Modeltime Workflow for Ensembles

Ensemble Calibration (4:45)
Ensemble Refitting, Method 1: Retraining Submodels Only (5:43)
Ensemble Refitting, Method 2: Retraining both Sub-Models & Super-Learners (5:33)

14.7 Saving Your Work

Save the Multi-Level Ensemble (1:27)
Object Size: 50MB! Here’s why. (3:15)

14.8 Code Checkpoint – Module 14 – Ensemble Methods

Code Checkpoint [File Download]

15.0 Forecasting at Scale – Time Series Groups [Panel Data]

Welcome to Module 15 – Forecasting at Scale using Panel Data (Non-Recursive) Strategies (2:30)
Setup [File Download] (4:30)

15.1 Data Understanding & Preparation

Data Understanding (4:33)
Data Prep, Part 1: Padding by Group | Ungrouped Log Transformation (3:53)
Data Prep, Part 2: Extend by Group (2:44)
Data Prep, Part 3: Fourier Features & Lag Features by Group (6:03)
Data Prep, Part 4: Rolling Features by Group | Adding a Row ID (4:59)
Future & Prepared Data – Preparation (7:34)

15.2 Time Splitting – Train/Test

Time Series Split (Train/Test) (3:50)

15.3 Preprocessing & Recipes

Cleaning Outliers by Group (5:18)
Recipe, Part 1: Time Series Calendar Features (3:24)
Recipe, Part 2: Normalization (Standardization) & Categorical Encoding (5:36)

15.4 Modeling: Make 7 Panel Models

Panel Model 1: Prophet with Regressors (2:11)
Panel Model 2: XGBoost (2:41)
Panel Model 3: Prophet Boost (1:57)
Panel Model 4: SVM (Radial) (2:02)
Panel Model 5: Random Forest (1:31)
Panel Model 6: Neural Net (1:27)
Panel Model 7: MARS (1:27)
Accuracy Check – This will help us select models for tuning (3:22)

15.5 Hyperparameter Tuning the Panel Models

Tuning Resamples: K-Fold Cross Validation (2:45)
Panel Model 8: XGBoost Tuned | Tunable Workflow Spec (3:37)
Panel Model 8: XGBoost Tuned | Hyperparameter Tuning (8:12)
Panel Model 9: Random Forest Tuned | Tunable Workflow Spec (1:56)
Panel Model 9: Random Forest Tuned | Hypeparameter Tuning (3:28)
Panel Model 10: MARS Tuned | Tunable Workflow Spec (2:00)
Panel Model 10: MARS Tuned | Hyperparameter Tuning (3:07)

15.6 Modeltime Panel Evaluation

Modeltime Table, Calibration & Accuracy for Panel Data [No Changes] (4:37)
Forecast Visualization for Panel Data [Use keep_data = TRUE] (4:23)

15.7 Time Series Cross Validation (Modeltime Resample)

Time Series Cross Validation (TSCV) (3:37)
Modeltime Fit Resamples (1:48)
Modeltime Resample Accuracy (3:53)
Plot Modeltime Resamples (2:15)

15.8 Ensemble Panel Models

Ensemble Average (Mean) & Sub-Model Selection (2:47)
Accuracy (Test Set, No Inversion) (1:18)
Forecast Visualization (Test Set, Inverted) (3:57)
Accuracy by Group (Test Set, Inverted): summarize_accuracy_metrics() [MAE 46] (4:29)
Refitted Ensemble & Future Forecast (6:11)
Ensemble Median: Avoid Overfitting (3:29)

15.9 Recap

Congrats – You Just Forecasted 20 Time Series Using Panel Data Techniques! (2:28)

15.10 Code Checkpoint – Panel Data

Code Checkpoint [File Download]

[PART 3] Deep Learning with GluonTS

Welcome to Part 3 – Deep Learning with GluonTS (0:53)
RStudio IDE Preview Version | Best for Working with Python

Module 16 – Setting Up GluonTS & Intro to Reticulate

What is a Python Environment? And, why do I need it?
Setup [File Download] (1:19)
R Package Installation Requirements (2:30)

16.1 Configuring the “r-gluonts” Python Environment

GluonTS Environment Setup Overview (2:10)
Installing the Python “r-gluonts” Environment (2:15)
Connecting to the “r-gluonts” Environment (2:48)
Troubleshooting Installation (2:50)

16.2 Testing Modeltime GluonTS

Deep Learning Experiment – Predict a Straight Line, Part 1 (3:08)
Deep Learning Experiment – Predict a Straight Line, Part 2 (3:32)

16.3 Getting to Know Reticulate & Your Python Environments

Managing Python Environments with Reticulate – Conda & Virtual Env (3:18)
Which Environment am I using & What’s in it? (4:43)

16.4 Using a Custom GluonTS Environment

Setting Up a Custom Python Environment (6:58)
Activating (Connecting to) a Custom Python Environment (5:39)
Reactivating the Default GluonTS Environment (2:13)

16.5 Code Checkpoint – GluonTS Environment Setup

Code Checkpoint [File Download]

Module 17 – Using GluonTS with Reticulate

GluonTS Deep Learning | Navigating the Documentation (4:46)
Setup & Introduction [File Download] (3:27)
Load Libraries (0:42)

17.1 Reticulated Python Basics

Reticulated Python, Part 1 (7:00)
Reticulated Python, Part 2 (4:36)

17.2 Data, Preprocessing, & GluonTS ListDatasets

Getting the Weekly Transactions Data (1:35)
Preparing the Full Data for Deep Learning (4:36)
Creating a GluonTS ListDataset from a Data Frame (Tibble) (3:10)
Examining a GluonTS ListDataset (5:33)
Converting from GluonTS ListDataset to Pandas Series (7:20)

17.3 DeepAR GluonTS Model

The DeepAREstimator & Trainer (8:43)
Making Our First DeepAR Model (5:14)
The Prediction (Generator) (3:27)
Probabilistic Forecasting (5:06)

17.4 Visualizing the Forecast | matplotlib, ggplot, & plotly

Matplotlib, Part 1 (5:06)
Matplotlib, Part 2 (3:47)
ggplot + plotly (Interactive), Part 1 (6:26)
ggplot + plotly (Interactive), Part 2 (4:43)

17.5 Introducing Modeltime GluonTS

Modeltime DeepAR | Workflow Benefits (6:56)
Modeltime DeepAR | Adding More Epochs (1:17)

17.6 Saving & Loading GluonTS Models

Save & Load | Using GluonTS & Reticulate (6:06)
Save & Load | Modeltime GluonTS Models (3:28)

17.7 Bonus!! GluonTS Deep Factor Models

Creating a DeepFactorEstimator (5:11)
Visualizing the Deep Factor Predictions with Matplotlib (3:17)

17.8 Conclusions & Pro/Cons

Reticulated GluonTS vs Modeltime GluonTS (Pros & Cons) (4:43)

17.9 Code Checkpoint – GluonTS Reticulate

Code Checkpoint [File Download]

Module 18 – Time Series Groups with Modeltime GluonTS

Deep Learning At Scale (with Modeltime GluonTS)
Setup [File Download] (2:52)

18.1 Data Collection & Initial Preparation

Getting the Data | GA Webpage Visits Daily (2:17)
Full Data | Padding the Data (4:02)
Alternative Padding Strategy
Full Data | Log1P Transformation (Target) (1:01)
Full Data | Extend (Future Frame) (1:41)
Full Data | Group-Wise Fourier Series (2:33)
Full Data | Group-Wise Adding Lagged Features (1:47)
Full Data | Group-Wise Rolling Features (3:10)
Full Data | Adding a Row ID (0:52)
Data Prepared | skimr::skim() – Watch out for missing data (2:11)
Future Data | skimr::skim() – Watch out for missing data (4:07)
Split Data Prepared (Train/Test) (2:15)
Visually Inspect the Train/Test Splits – Inspect for missing groups (3:37)

18.2 Deep Learning Models – DeepAR

Modeltime GluonTS Recipe (4:07)
DeepAR (Model 1) | Understanding deep_ar() & Training Our 1st Model (9:56)
DeepAR (Model 1) | Model Accuracy Evaluation (MAE 0.546) (4:07)
Ahhh My Model Errored (Skimr to the Rescue!) (3:59)
DeepAR (Model 2) | Adjusting Hyperparameters (4:19)
DeepAR (Model 2) | Model Accuracy Evaluation (MAE 0.537) (1:49)
DeepAR (Model 3) | Scaling by Group (3:31)
DeepAR (Model 3) | Model Accuracy (MAE 0.509) (1:17)

18.3 Deep Learning Models – N-BEATS

N-BEATS (Model 4) | Understanding nbeats() & Training Our 1st N-BEATS Model (9:57)
N-BEATS (Model 5) | Improving our model with a new loss_function (MAE 0.611) (4:25)
N-BEATS (Model 6) | Ensemble Multiple N-BEATS (7:09)
N-Beats (Model 6) | Model Accuracy (MAE: 0.544) (3:04)
Future Forecast | Inspect Refitted Models (6:01)

18.4 Machine Learning – XGBoost

Setting up the Parallel Processing Backend (1:33)
Recipes for ML (XGBoost Model) (7:01)
XGBoost Tunable Model Spec (2:34)
Hyperparameter Tuning the XGBoost Model (6:20)

18.5 Evaluating Our ML & DL Models

Evaluate Accuracy on the Testing Set (MAE: 0.527) (4:35)
Visualize the Testing Set Forecast (2:46)
Refit & Visualize the Future Forecast (2:40)

18.6 Ensembles of ML & DL Models [The Best of Both Worlds]

Ensembles | Combining ML & DL (MAE: 0.496) (5:54)
Ensemble | Refitting & Forecasting the Future (4:31)

18.7 DL Wrapup – Saving/Loading Models & Conclusions

Saving | Ensemble & Submodels (5:59)
Loading | Ensemble & Submodels (4:23)
Conclusions | Deep Learning with Modeltime & GluonTS (2:40)
Code Checkpoint [File Download]

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SPECIAL Modeltime Ecosystem: Bonus Lessons:

About the Special Bonus Lessons
Hierarchical Forecasting with Modeltime (105:37)
Modeltime H2O: Forecasting with H2O AutoML (63:43)
Modeltime Recursive: Autoregressive Forecasting (Lags < Forecast Horizon) | Energy Demand (95:16)
Forecasting Airline Passengers Covid-19 | Modeltime 0.7.0 Updates | PyTorch, GluonTS, Global Baselines (93:34)
How to Forecast 100 Time Series | Modeltime Nested (Iterative) Forecasting (113:01)

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