DS4B 203-R: High-Performance Time Series Forecasting

Become the time-series domain expert for your organization

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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.


Discover What's Inside the High-Performance Time Series Forecasting Course


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)

Get started now!



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

$10,000 Value

Purchase today for: $499

Get started now!



Your Instructor


Matt Dancho
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


  Prerequisites
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  Getting Help
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  Module 0 - Introduction to High-Performance Forecasting
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  0.2 Course Projects - Google Analytics, Email Subscribers, & Sales Forecasting
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  Module 01 - Time Series Jumpstart
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  1.3 TS Jumpstart: Dive into Forecasting Email Subscribers!
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  1.3.2 Evaluation & Train/Test Windows
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  1.3.3 Forecasting with Prophet
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  1.3.5 Recap & Code Checkpoint - Module 01 - TS Jumpstart
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  โœจ[Part 1] Time Series with Timetk
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  2.3 Seasonality Plots
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  2.5 STL Decomposition & Regression Plots
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  2.6 Regression Plots [SECRET WEAPON FOR FEATURE ENGINEERING]
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  2.7 Code Checkpoint - Module 02 - Visualization
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  Module 03 - Time Series Data Wrangling
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  3.2 Pad by Time
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  3.3 Filter By Time
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  3.4 Mutate By Time
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  3.7 Forecasting with Future Frames ๐Ÿ“ˆ
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  3.8 Code Checkpoint - Module 03 - Data Wrangling
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  Module 04 - Transformations for Time Series
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  4.6 Fourier Series [MUST KNOW] ๐Ÿ’ก
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  4.8 Code Checkpoint - Module 04 - Transformations
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  โ›ฐ๏ธ Challenge #1 - Exploring Transactions & Web Page Traffic
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  5.2 Interactions
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  5.4 Autocorrelated Lag Features
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  5.7 Recommended Model Features
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  5.8 Code Checkpoint - Module 05 - Introduction to Feature Engineering
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  6.2 Separate into Modeling Data & Forecast Data
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  6.3 Separate into Training Data & Testing Data
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  6.10 Code Checkpoint - Module 06 - Advanced Feature Engineering
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  โ›ฐ๏ธ Challenge #2 - Feature Engineering & Modeltime Workflow [YOU'VE GOT THIS!]
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  Part 1 Complete - You rock! ๐Ÿ™Œ๐Ÿ™Œ๐Ÿ™Œ
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  โœจ[Part 2] Machine Learning for Time Series with Modeltime
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  7.2 Modeltime Table
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  7.3 Calibration Table
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  7.5 Forecasting the Test Data
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  7.6 Model Refitting & Forecasting
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  7.7 Code Checkpoint - Module 07A - Modeltime Workflow [In-Depth]
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  7.9 Code Checkpoint - Module 07B - Modeltime New Features!
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  8.5 Code Checkpoint - Module 08 - ARIMA
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  9.5 Checkpoint - Module 09 - Prophet
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  Module 10 - Exponential Smoothing, TBATS, & Seasonal Decomposition
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  10.5 Recap - ETS, TBATS, Seasonal Decomp
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  10.6 Code Checkpoint - Module 10 - ETS, TBATS, & Seasonal Decomposition
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  11.0 Machine Learning Algorithms [IMPORTANT] ๐Ÿ’ก
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  *** Plotting Utility *** - Let's make a helper function to speed evaluation up!
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  11.5 K-Nearest Neighbors (KNN) - Similarity (Distance) Based
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  You're kicking butt... But, don't forget to take breaks
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  11.9 Neural Net (NNET) - Like a Linear Regression but Better
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  11.12 Saving Your Work - Artifacts!
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  11.13 Checkpoint - Module 11 - Machine Learning Algorithms
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  12.0 Boosted Algorithms - Prophet Boost & ARIMA Boost
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  12.4 Code Checkpoint - Boosted Algorithms
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  13.3.2 Prophet Boost Tuning, Round 2 - Controlling Learning Rate
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  13.4 Saving Our Progress
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  13.5 Code Checkpoint - Model 13 - Hyperparameter Tuning
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  14.8 Code Checkpoint - Module 14 - Ensemble Methods
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  15.0 Forecasting at Scale - Time Series Groups [Panel Data]
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  15.2 Time Splitting - Train/Test
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  15.10 Code Checkpoint - Panel Data
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  [PART 3] Deep Learning with GluonTS
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Frequently Asked Questions


When does the course start and finish?
The course starts now and never ends! It is a completely self-paced online course - you decide when you start and when you finish.
How long do I have access to the course?
How does lifetime access sound? After enrolling, you have unlimited access to this course for as long as you like - across any and all devices you own.
What if I am unhappy with the course?
We would never want you to be unhappy! If you are unsatisfied with your purchase, contact us in the first 30 days and we will give you a full refund.
Is the R for Business 101 Course a Prerequisite?
Yes. I expect that you know basic R, the tidyverse, and have had previous exposure to the tidymodels ecosystem. While I teach every time series visualization, transformation, feature engineering, and modeling algorithm in-depth, I depend on your knowledge of non-time series skills like data wrangling and basic machine learning. If you feel like you don't need 101, my suggestion is to try the Time Series Course. You can always work backwards and take the 101-course as-needed.
Will there be any additional costs in the course?
I'm happy to say that all of the tools I cover are 100% Free & Open Source. I am the creator of 2 of the key libraries: modeltime & timetk. The 3rd, GluonTS, is built and actively maintained by Amazon as part of their MXNet ecosystem. So you can feel assured that you are learning amazing software at zero extra cost.