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High Performance Time Series

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Original price was: $709.00.Current price is: $176.00.

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.

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Description

High Performance Time Series

High Performance Time Series

High Performance Time Series

Become the time-series domain expert for your organization

Become the Time Series Expert
for your organization

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.

1 Time Series Feature Engineering

2 Machine Learning for Time Series

3 Deep Learning for Time Series

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)!

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)
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)
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)

Get immediately download High Performance Time Series

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]
CONGRATULATIONS!!! You. Did. It.
  • WOO HOO!!! Get YOUR Certificate & a discount on your next purchase! (1:07)
✨SPECIAL Modeltime Ecosystem: Bonus Lessons
  • Preview

    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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