High Performance Time Series

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This course is on the market for quick supply! The High-Performance Time Series Forecasting Course is an incredible course designed to show Enterprise Analysts and Knowledge Scientists how one can scale back forecast error utilizing state-of-the-art forecasting strategies which have gained competitions.

High Performance Time Series

High Performance Time Series

Turn out to be the time-series area knowledgeable on your group

Turn out to be the Time Series Skilled
on your group

The High-Performance Time Series Forecasting Course is an incredible course designed to show Enterprise Analysts and Knowledge Scientists how one can scale back forecast error utilizing state-of-the-art forecasting strategies which have gained competitions. You will endure a full transformation studying probably the most in-demand expertise that organizations want proper now. Time to speed up your profession.

Crafted For Enterprise Analysts & Knowledge Scientists

That want to cut back forecasting error and scale outcomes on your group.

That is presumably my most difficult course ever. You will be taught the time collection expertise which have taken me 10-years of research, apply, and experimentation.

My speak on High-Performance Time Series Forecasting

This course provides you the instruments you’ll want to meet at the moment’s forecasting calls for.

A full yr was spent on constructing two of the software program packages you may be taught, modeltime and timetk.

Plus, I am educating you GluonTS, a state-of-the-art deep studying framework for time collection written in python.

This course will problem you. It’ll change you. It did me.

– Matt Dancho, Course Teacher & Founding father of Enterprise Science

Bear a Full Transformation
By studying forecasting strategies that get outcomes

With High-Performance Forecasting, you’ll endure a whole transformation by studying probably the most in-demand expertise for creating high-accuracy forecasts.

By way of this course, you’ll be taught and apply:

Machine Studying & Deep Studying
Characteristic Engineering
Visualization & Knowledge Wrangling
Transformations
Hyper Parameter Tuning
Forecasting at Scale (Time Series Teams)

The way it works

Your path to turning into an Skilled Forecaster is simplified into 3 streamlined steps.

Time Series Characteristic Engineering

Machine Studying for Time Series

Deep Studying for Time Series

Half 1

Time Series Characteristic Engineering

First, we construct your time collection function engineering expertise. You be taught:

Visualization: Figuring out options visually utilizing the best plotting strategies
Knowledge Wrangling: Aggregating, padding, cleansing, and increasing time collection information
Transformations: Rolling, Lagging, Differencing, Creating Fourier Series, and extra
Characteristic Engineering: Over 3-hours of content material on introductory and superior function engineering

Half 2

Machine Studying for Time Series

Subsequent, we construct your time collection machine studying expertise. You be taught:

17 Algorithms: 8 hours of content material on 17 TOP Algorithms. Divided into 5 teams:
- ARIMA
- Prophet
- Exponential Smoothing – ETS, TBATS, Seasonal Decomposition
- Machine Studying – Elastic Internet, MARS, SVM, KNN, Random Forest, XGBOOST, Cubist, NNET & NNETAR
- Boosted Algorithms – Prophet Increase & ARIMA Increase
Hyper Parameter Tuning: Methods to cut back overfitting & improve mannequin efficiency
Time Series Teams: Scale your evaluation from one time collection to a whole lot
Parallel Processing: Wanted to hurry up hyper parameter tuning and forecasting at scale
Ensembling: Combining many algorithms right into a single tremendous learner

Half 3

Deep Studying for Time Series

Subsequent, we construct your time collection deep studying expertise. You be taught:

GluonTS: A state-of-the-art forecasting bundle that is constructed on prime of mxnet (made by Amazon)
Algorithms: Be taught DeepAR, DeepVAR, NBEATS, and extra!

Challenges & Cheat Sheets

Subsequent, we construct your time collection machine studying expertise. You be taught:

Cheat Sheets: Developed to make your forecasting workflow reproducible on any downside
Challenges: Designed to check your skills & solidify your information

Abstract of what you get

A methodical coaching plan that goes from idea to manufacturing ($10,000 worth)
- Half 1 – Characteristic Engineering with Timetk
- Half 2 – Machine Studying with Modeltime
- Half 3 – Deep Studying with GluonTS
- Challenges & Cheat Sheets

Your Teacher

Matt Dancho

Founding father of Enterprise Science and common enterprise & finance guru, He has labored with many purchasers from Fortune 500 to high-octane startups! Matt loves educating information scientists on how one can apply highly effective instruments inside their group to yield ROI. Matt does not relaxation till he will get outcomes (actually, he does not sleep so do not be suprised if he responds to your e-mail at 4AM)!

Get instantly obtain High Performance Time Series

Course Curriculum

Welcome to High Performance Time Series!

High-Performance Time Series – Turn out to be the Time Series Skilled for Your Group (2:34)
Non-public Slack Channel – How you can Be part of
Video Subtitles (Captions)
What’s a High-Performance Forecasting System?
[IMPORTANT] System Necessities – R + Python Necessities & Frequent Points

Stipulations

Prerequisite – Knowledge Science for Enterprise Half 1

Getting Assist

Getting Assist (IMPORTANT!!!)

Module 0 – Introduction to High-Performance Forecasting

High-Performance Forecasting – What You are Studying, Why You are Studying It (0:43)

0.1 Forecast Competitors Evaluation

The Forecasting Competitors Evaluation & Course Development (3:34)
2014 Kaggle Walmart Recruiting Problem (5:11)
2018 M4 Competitors (3:37)
2018 Kaggle Wikipedia Web site Site visitors Forecasting Competitors (4:30)
2020 M5 Competitors (5:59)
5 Key Takeaways from the Forecast Competitors Evaluation (5:41)

0.2 Course Tasks – Google Analytics, E-mail Subscribers, & Gross sales Forecasting

The Enterprise Case – Growing a Finest-in-Class Forecasting System (3:03)

0.3 What Instruments are in Your Toolbox?

Timetk: Time Series Knowledge Preparation, Visualization, & Preprocessing (5:54)
Modeltime: Time Series Machine Studying (5:25)
GluonTS: Time Series Deep Studying (2:01)
?️ [Cheat Sheet] Forecasting Workflow

Module 01 – Time Series Leap

Time Series Leap (0:54)

1.1 Time Series Venture Setup

Venture Setup (2:28)
Course Knowledge (File Obtain) (1:02)
R Package deal Set up – Half 1 (File Obtain) (5:26)
R Package deal Set up – Half 2 (5:14)
Leap Setup (File Obtain) (0:44)

1.2 Enterprise Understanding & Dataset Terminology

Set up Relationships, Half 1 – Google Analytics Abstract Dataset (4:11)
Set up Relationships, Half 2 – Google Analytics High 20 Pages (5:23)
Construct Relationships – Mailchimp & Studying Lab Occasions (4:49)
Generate Course Income – Transaction Income & Product Occasions (3:03)
Code Checkpoint (File Obtain) (0:54)

1.3 TS Leap: Dive into Forecasting E-mail Subscribers!

Learn This! – Time Series Leap Intent
Time Series Leap – Setup (File Obtain) (3:20)
Libraries & Knowledge (3:13)

1.3.1 Exploratory Knowledge Evaluation for Time Series

EDA for Time Series (1:08)
Summarize By Time (5:46)
Time Series Abstract Diagnostics (4:47)
Pad by Time (4:08)
Visualize the Time Series (3:12)

1.3.2 Analysis & Prepare/Take a look at Home windows

Analysis Window – Filter By Time (4:43)
Time Series Prepare/Take a look at Cut up (4:53)

1.3.3 Forecasting with Prophet

Coaching a Prophet Mannequin with Modeltime (4:21)
Modeltime Forecasting Workflow – Spherical 1 (7:43)

1.3.4 Forecasting with Characteristic Engineering

Visualizing Seasonality (4:34)
Characteristic Engineering – Half 1 (5:45)
Characteristic Engineering – Half 2 (5:51)
Machine Studying with Workflows (3:35)
Modeltime Forecasting Workflow – Spherical 2 (5:59)

1.3.5 Recap & Code Checkpoint – Module 01 – TS Leap

Here is the place you’re going. (3:11)
Code Checkpoint (File Obtain)

✨[Part 1] Time Series with Timetk

Welcome to Half 1 – Time Series with Timetk! (2:17)

Module 02 – Time Series Visualization

Setup (File Obtain) & Overview – Visualization (2:11)
Knowledge Preparation – Half 1 (4:29)
Knowledge Preparation – Half 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 Teams (1:54)
Interactive & Static Plots (2:00)

2.2 Autocorrelation Plots

ACF & PACF Ideas – Autocorrelation & Partial Autocorrelation
ACF & PACF Plotting (7:49)
Lag Adjustment (1:24)
CCF Plotting – Cross Correlations (7:58)

2.3 Seasonality Plots

Seasonality Field Plot (5:52)
Seasonality Violin Plot (0:53)

2.4 Anomaly Plots

Anomaly Plot Fundamentals (4:50)
Getting the Anomaly Knowledge (2:00)
Working with Grouped Knowledge (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 Obtain)

Module 03 – Time Series Knowledge Wrangling

Setup (File Obtain) & Overview – Knowledge Wrangling (2:34)

3.1 Summarise By Time [MUST KNOW] ?

Single & Grouped Time Series Summarizations (4:37)
Utilizing Throughout (to Summarize Vast-Format Tibbles by Time) (5:11)
Weekly/Month-to-month/Quarterly/Yearly Aggregations (3:33)
Ground, Ceiling, Spherical (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 Imply, Median, Max, Min By Time (7:57)

3.5 Becoming a member of By Time

Combining Subscribers & Internet Site visitors (3:48)
Inspecting the Be part of (3:00)
Formatting the Be part of for Characteristic Relationships (5:49)
Be part of Cross Correlations (3:22)

3.6 Time Series Index Operations

Making a Time Series (4:39)
Making a Vacation Sequence (3:14)
Time Offsets (3:01)
Making a Future Time Series (3:12)

3.7 Forecasting with Future Frames ?

The Future Body (2:47)
[FORECAST SPOTLIGHT] Forecasting with the Future Body ? (6:53)

3.8 Code Checkpoint – Module 03 – Knowledge Wrangling

Code Checkpoint (File Obtain)

Module 04 – Transformations for Time Series

Setup (File Obtain) & Overview – Transformations (2:15)
Libraries & Knowledge (2:12)

4.1 Variance Discount Transformations – Log & Field Cox [MUST KNOW] ?

Why is Variance Discount Vital? (4:43)
Log – Log (and Log1P) Transformation (4:17)
Log – Assessing the Advantage of Log1P Transformation (2:51)
Log – Teams & Inversion (3:43)
Field Cox – What’s the Field Cox Transformation? (2:34)
Field Cox – Assessing the Profit (4:04)
Field Cox – Inversion (2:05)
Field Cox – Managing Grouped Transformations & Inversion (8:36)

4.2 Rolling & Smoothing Transformations

Introduction to Rolling & Smoothing (1:49)
Rolling Home windows – What’s a Transferring Common? (File Obtain) (3:53)
Rolling Home windows – Transferring Common & Median Utilized (8:53)
Loess Smoother (7:02)
Rolling Correlation – Slidify, Half 1 (4:16)
Rolling Correlation – Slidify, Half 2 (7:40)
[BUSINESS SPOTLIGHT] The Downside with Forecasting utilizing a Transferring Common (6:43)

4.3 Vary Discount Transformations

Introduction to Normalization & Standardization (0:59)
What’s Normalization? [Min = 0, Max = 1] (4:50)
What’s Standardization? [Mean = 0, Standard Deviation = 1] (2:31)

4.4 Imputation & Outlier Cleansing

Introduction to Imputation & Outlier Cleansing (0:44)
Imputation – Time Series NA Restore (6:40)
Anomalies – Time Series Outlier Cleansing (7:22)
Anomalies – When to Take away Outliers (5:21)

4.5 Lags & Differencing Transformations [MUST KNOW] ?

Introduction to Lags & Differencing (1:08)
Lags – What’s a Lag? (1:49)
Lags – Lag Detection with ACF/PACF (3:54)
Lags – Regression with Lags (5:06)
Differencing – Development vs Change (4:00)
Differencing – Acceleration (6:22)
Differencing – Evaluating A number of 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’s the Log Interval Transformation? (5:47)
Visualizing the Transformation (4:12)
Transformations & Preprocessing (5:09)
Modeling (6:29)
Making ready Future Knowledge (3:36)
Making Predictions (1:05)
Combining the Forecast Knowledge (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 Obtain)

⛰️ Problem #1 – Exploring Transactions & Internet Web page Site visitors

Problem #1 Dialogue (File Obtain) (4:21)
Answer – Half 1 (File Obtain) (7:18)
Answer – Half 2: Begins at “Identify Relationships” (7:51)

Module 05 – Introduction to Characteristic Engineering (for Time Series)

Setup (File Obtain) & Overview – Intro to Characteristic Engineering (2:30)
Knowledge Prep, Half 1 – Log Standardize (5:27)
Knowledge Prep, Half 2 – Getting Able to Clear (5:01)
Knowledge Prep, Half 3 – Focused Cleansing with Between Time (4:18)

5.1 Time-Primarily based Options (Pattern & Seasonal/Calendar) [MUST KNOW] ?

The Time Series Signature (7:55)
Characteristic Removing (3:28)
Linear Pattern (2:10)
Non-Linear Pattern – Foundation Splines (4:41)
Non-Linear Pattern – Pure Splines (Stiffer than Foundation Splines) (4:29)
Seasonal Options – Weekday & Month (3:21)
Seasonal Options – Combining with Pattern (5:23)

5.2 Interactions

Interplay Options – Spikes Each Different Wednesday (7:35)

5.3 Fourier Options

Choosing & Including Fourier Frequency Options (4:21)
Modeling & Visualizing the Fourier Results (2:07)

5.4 Autocorrelated Lag Options

Choosing & Including Lag Options (6:59)
Modeling & Visualizing the Lag Results (5:20)

5.5 Particular Occasion Options

Making ready Occasion Knowledge for Evaluation (6:34)
Visualizing Occasions (2:57)
Modeling & Visualizing Occasion Results (2:08)
Fixing the Spline (2:07)

5.6 Exterior Regressors (Xregs)

Remodeling Xregs (5:05)
Becoming a member of Xregs (1:49)
Analyzing Cross Correlations (1:53)
Modeling with Xregs (3:28)
Visualizing PageViews vs Optins & Modeling Lags (6:58)

5.7 Really useful Mannequin Options

Amassing the Really useful Mannequin (3:44)
Saving the Mannequin Artifact (2:28)

5.8 Code Checkpoint – Module 05 – Introduction to Characteristic Engineering

Code Checkpoint (File Obtain)

Module 06 – Superior Characteristic Engineering Workflow

Forecasting Workflow [CHEAT SHEET] ?️ (3:40)
Setup (File Obtain) & Overview – Superior Characteristic Engineering (1:43)
Knowledge Preparation (4:42)

6.1 Creating the “Full” Dataset – Extending & Including Lagged Options & Occasions [IMPORTANT] ?

The “Full” Dataset (2:50)
Extending – Future Body (3:21)
Including Lag Options (4:02)
Add Lagged Rolling Options (5:03)
Add Occasions (Exterior Regressors) (2:57)
Format Column Names (3:09)

6.2 Separate into Modeling Knowledge & Forecast Knowledge

Knowledge Ready / Future Knowledge Cut up (2:48)

6.3 Separate into Coaching Knowledge & Testing Knowledge

Prepare / Take a look at Cut up (3:55)

6.4 Recipes – Characteristic Engineering Pipeline Steps

Recipes Intro (2:41)
Step – Time Series Signature Options (5:48)
Step – Characteristic Removing (3:10)
Step – Standardization (2:11)
Step – One-Scorching Encoding (1:55)
Step – Interplay Options (2:28)
Step – Fourier Series Options (2:03)

6.5 Constructing the Spline Mannequin

Mannequin Spec: LM Mannequin (1:02)
Recipe Spec: Spline Options (5:59)
Workflow: Spline Recipe + LM Mannequin (2:49)

6.6 Introduction to Modeltime Workflow

Modeltime Desk & Calibration (2:08)
Forecasting the Take a look at Knowledge (2:40)
Measuring the Take a look at Accuracy (1:19)
Evaluating the Coaching & Testing Accuracy (1:32)

6.7 Constructing the Lag Mannequin

Recipe Spec: Lag Options (3:00)
Workflow: Lag Recipe+ LM Mannequin (2:40)
Modeltime: Evaluating Spline & Lag Fashions (4:23)

6.8 Forecasting the Future

Refitting the Fashions (4:37)
Transformation Inversion (5:23)
Visualizing the Forecast within the Authentic Scale (1:59)

6.9 Saving the Artifacts

Creating an Artifact Checklist, Half 1 (4:34)
Creating an Artifact Checklist, Half 2 (3:11)
Organizing the Artifacts Checklist (1:57)
Saving the Artifacts (1:28)

6.10 Code Checkpoint – Module 06 – Superior Characteristic Engineering

Code Checkpoint (File Obtain)

⛰️ Problem #2 – Characteristic Engineering & Modeltime Workflow [YOU’VE GOT THIS!]

Problem Dialogue, Half 1 (File Obtain) – Characteristic Preparation (5:11)
Problem Dialogue, Half 2 – Characteristic Engineering & Modeling (4:56)

Problem #2 – Answer

Answer, Half 1 (File Obtain) – Accumulate & Put together Knowledge (3:49)
Answer, Half 2 – Visualizations (3:19)
Answer, Half 3A – Create Full Dataset (5:46)
Answer, Half 3B – Visualize the Full Dataset (3:47)
Answer, Half 4 – Mannequin/Forecast Knowledge Cut up (1:05)
Answer, Half 5 – Prepare/Take a look at Knowledge Cut up (0:56)
Answer, Half 6 – Characteristic Engineering (4:18)
Answer, Half 7 – Modeling: Spline Mannequin (6:08)
Answer, Half 8 – Modeling: Lag Mannequin (2:25)
Answer, Half 9 – Modeltime (4:03)
Answer, Half 10 – Forecast (6:49)

Problem #2 Bonus – Regularization

Regularization, Half 1 (File Obtain) – Mannequin: GLMnet (4:01)
Regularization, Half 2 – Enhancing the Lag Mannequin with GLMNet (5:28)
Regularization, Half 3 – Forecasting the Future Knowledge with GLMNet + Lag Recipe (3:02)

Half 1 Full – You rock! ???

WOOO HOOO – You crushed it!

✨[Part 2] Machine Studying for Time Series with Modeltime

Choosing Up From Half 1 (Venture Obtain)

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 Fashions – Object Varieties & Necessities

Mannequin Necessities for Modeltime (1:34)
Parsnip Object Fashions – Univariate (3:37)
Workflow Objects – Multivariate, Date-Primarily based Options (7:14)
Workflow Object – Multivariate, Exterior Options (4:53)

7.2 Modeltime Desk

Modeltime Desk – Key Necessities (4:27)

7.3 Calibration Desk

Calibration Desk – How It Works (3:29)

7.4 Measuring Mannequin Accuracy [IMPORTANT!!!]

Major Accuracy Metrics & Makes use of [SUPER IMPORTANT] (7:40)
Customized Metric Units utilizing Yardstick (3:54)
Customizing the Accuracy Desk Output (3:28)

7.5 Forecasting the Take a look at Knowledge

Modeltime Forecast – How It Works (6:22)
Customizing the Forecast Visualization (5:00)

7.6 Mannequin 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 Obtain)

7.8 New Options of Modeltime 0.1.0 – Module 07B ?

Setup (File Obtain) – Modeltime New Options (1:53)
Expedited Forecasting – Modeltime Desk (5:20)
Expedited Forecasting – Skip Straight to Forecasting (2:20)
Visualizing a Fitted Mannequin (2:57)
Calibration – In-Pattern vs Out-of-Pattern 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 Options!

Code Checkpoint (File Obtain)

Module 08 – ARIMA

Setup (File Obtain) (0:40)
ARIMA Coaching Overview (1:29)
Libraries & Artifacts Setup (1:49)

8.1 ARIMA Ideas ?

Auto-Regressive Features: ar() & arima() (5:15)
Auto-Regressive (AR) Modeling with Linear Regression (3:11)
Single-Step Forecast for AR Fashions (4:43)
Multi-Step Recursive Forecasting for AR Fashions (4:44)
Integration (Differencing) (5:42)
Transferring Common (MA) Course of (Error Modeling) (7:36)
Seasonal ARIMA (SARIMA) (4:29)
Including XREGS (SARIMAX) (4:44)

8.2 ARIMA in Modeltime

Setting Up Primary ARIMA in Modeltime (4:45)
Attempting Completely different ARIMA Parameters (5:11)
About AIC (Akaike Data Criterion) (3:42)

8.3 Modeltime Auto ARIMA

Implementing Auto ARIMA in Modeltime (1:49)
How Auto ARIMA Works – Lazy Grid Search (1:27)
Evaluating ARIMA & Auto ARIMA (3:15)
Including Fourier Options to Decide Up Greater than 1 Seasonality (3:49)
Including Occasion Options to Enhance R-Squared (Variance Defined) (1:33)
Refitting & Reviewing the Forecast (2:57)
Including Month Options to Account for February Enhance – BEST MAE 0.564 (3:35)

8.4 Recap – ARIMA

ARIMA Strengths & Weaknesses (and Methods that Labored) (3:56)
Saving Artifacts – Finest ARIMA Mannequin (3:28)

8.5 Code Checkpoint – Module 08 – ARIMA

Code Checkpoint (File Obtain)

Module 09 – Prophet

Setup (File Obtain) (0:27)
Prophet Coaching 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 Ideas ?

Extracting the Prophet Mannequin from Modeltime (3:11)
Visualizing the Impact of Key Parameters on the Prophet Mannequin (5:48)
Understanding Prophet Elements & Additive Mannequin (2:37)

9.3 Again to Modeling with Prophet – XREGS!

Becoming Prophet w/ Occasions (2:19)
Evaluating No Occasions vs Occasions – BEST MAE 0.488 (w/ Occasions) ? (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 Obtain)

Module 10 – Exponential Smoothing, TBATS, & Seasonal Decomposition

Setup (File Obtain) (0:18)
Overview – Exponential Smoothing (0:35)
Libraries & Artifacts (1:37)

10.1 Exponential Smoothing

The Exponential Weighting Perform (4:50)
Making use of the Exponential Weighting Perform to Make a Forecast (2:41)
ETS Mannequin: exp_smoothing() (3:52)
Visualizing the ETS Mannequin (4:48)

10.2 TBATS

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

10.3 Seasonal Decomposition Fashions

Seasonal Decomposition & A number of Seasonality Time Series (MSTS) Objects (2:28)
STLM ETS Mannequin (2:33)
STL Plot & Relationship to STLM ETS Mannequin (2:49)
STLM ARIMA Mannequin (1:55)
STLM ARIMA – Including XREGS (1:08)

10.4 Analysis

Making ready the Take a look at Forecast Visualization (3:30)
Evaluating A number of Fashions – ETS, TBATS, STLM ARIMA & ETS – BEST MAE 0.523 (TBATS) ? (3:45)
Refitting – Analyzing 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 Obtain)

⛰️ Problem #3 – ARIMA + Prophet + ETS + TBATS

Problem #3 Dialogue, Half 1 (File Obtain) – by way of ARIMA (5:32)
Problem #3 Dialogue, Half 2 – Prophet to Finish of Problem (2:33)

Get instantly obtain High Performance Time Series

Problem #3 – Answer

Answer, Half 1 – Prepare/Take a look at Setup (Answer File Obtain) (1:55)
Answer, Half 2 – ARIMA (Mannequin 1): Primary Auto ARIMA (3:03)
Answer, Half 3 – ARIMA (Mannequin 2): Auto ARIMA + Including Product Occasions (2:14)
Answer, Half 4 – ARIMA (Mannequin 3): Auto ARIMA + Occasions + Seasonality (2:08)
Answer, Half 5 – ARIMA (Mannequin 4): Forcing Seasonality with Guide ARIMA (1:17)
Answer, Half 6 – ARIMA (Mannequin 5): Auto ARIMA + Occasions + Fourier Series (0:57)
Answer, Half 7 – ARIMA – Modeltime Workflow (2:26)
Answer, Half 8 – ARIMA – Forecast Evaluation (3:18)
Answer, Half 9 – Prophet Fashions: Primary (6), Yearly Seasonality (7), Occasions (8), Occasions + Fourier (9) (2:52)
Answer, Half 10 – Prophet – Modeltime Workflow (1:38)
Answer, Half 11 – Prophet – Forecast Evaluation (3:13)
Answer, Half 12 – Exponential Smoothing Fashions: ETS (10), TBATS (11) (3:24)
Answer, Half 13 – Exponential Smoothing – Modeltime Workflow (1:45)
Answer, Half 14 – Exponential Smoothing – Forecast Evaluation (1:30)
Answer, Half 15 – Forecasting the Future Knowledge – ARIMA, Prophet & ETS/TBATS (3:40)
Answer, Half 16 – Last Evaluation – ARIMA, Prophet, & ETS/TBATS (2:47)

Problem #3 BONUS – ARIMA & Prophet vs Linear Mannequin

Bonus, Half 1 (File Obtain) – Including the LM from Problem #2 (4:43)
Bonus, Half 2 – Why is the LM forecast excessive in March? (4:41)

11.0 Machine Studying Algorithms [IMPORTANT] ?

Welcome to Machine Studying for Time Series (File Obtain) (5:22)

11.1 Elastic Internet Algorithm (GLMNet) – Linear

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

*** Plotting Utility *** – Let’s make a helper perform to hurry analysis up!

calibrate_and_plot() (5:50)
Visualizing the Impact of Parameter Changes (3:19)

11.2 A number of Adaptive Regression Splines (MARS) – Linear

We come from MARS (3:30)
MARS – A Easy Instance (6:55)
MARS – Spline & Lag Fashions – BEST MAE 0.518 (Spline Mannequin) ? (4:28)

11.3 Assist Vector Machine (SVM) – Polynomial

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

11.4 Assist Vector Machine (SMV) – Radial Foundation Perform

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

11.5 [Important Concept] KNN & Tree-Primarily based Algorithms – The Downside with Predicting Time Series Traits

Strengths/Weak spot – KNN & Tree-Primarily based Algorithms Cannot Predict Past the Min/Max (1:24)
KNN vs GLMNET – Making Pattern Knowledge with Pattern (2:08)
KNN vs GLMNET – Making Easy Pattern Fashions (4:12)
KNN vs GLMNET – Visualize the Pattern Predictions w/ Modeltime – Yikes, GLMNET simply schooled KNN (4:14)

11.5 Ok-Nearest Neighbors (KNN) – Similarity (Distance) Primarily based

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

You are kicking butt… However, remember to take breaks

[COFFEE BREAK] With Invoice Murray

11.6 Random Forest (Tree-Primarily based)

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

11.7 XGBoost (Gradient Boosting Machine) – Tree-Primarily based

XGBoost – Spline & Lag Fashions (5:00)
XGBoost – Tweaking Parameters – 0.484 MAE (Lag Mannequin) (6:35)
XGBoost – Tweaking Parameters 2 – BEST MAE 0.484 (Lag Mannequin) ? (3:32)

11.8 Cubist – Combo of Timber (Guidelines) + Linear Fashions at Nodes

Cubist – Spline & Lag Fashions – 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 Internet (NNET) – Like a Linear Regression however Higher

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

11.10 NNETAR – Combining AR Phrases with a NNET!

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

11.11 Modeltime Experimentation Evaluation

Organizing in a Modeltime Desk (4:22)
Updating the Descriptions Programmatically (4:02)
Mannequin Choice – Course of & Suggestions (utilizing Accuracy Desk) (3:39)
Mannequin Inspection – Course of & Suggestions (utilizing Take a look at Forecast Visualization) (3:03)
Mannequin Inspection – Visualizing the Future Forecast (5:42)

11.12 Saving Your Work – Artifacts!

Saving Fashions (2:34)
Saving your calibrate_and_plot() perform (1:29)

11.13 Checkpoint – Module 11 – Machine Studying Algorithms

Code Checkpoint (File Obtain)

12.0 Boosted Algorithms – Prophet Increase & ARIMA Increase

Boosted Algorithms – A Highly effective Approach for Enhancing Performance (3:37)

12.1A Prophet Baseline Mannequin

Baseline: Finest Prophet Mannequin (2:38)
? [Pro Tip] How you can Repair a Damaged Mannequin (2:50)
Prophet Baseline – Finest Mannequin MAE 0.488 (0:54)

12.1B Prophet Increase

Recipe for Prophet Increase (3:33)
Mannequin Technique – Utilizing XGBOOST for Seasonality/XREG Modeling (4:39)
Workflow – No Parameter Tweaking (3:41)
? [KEY CONCEPT] Prophet Increase – Modeling Pattern with Prophet, Residuals with XGBoost (3:00)
Prophet Increase – Tweaking Parameters – BEST MAE 0.457 ? (6:33)

12.2 ARIMA Increase

Modeling Technique – ARIMA for pattern, XGBOOST for XREGS (3:50)
ARIMA Increase – Mannequin Specification (5:57)
ARIMA Increase – Tweaking Parameters – BEST MAE 0.523 (4:34)

12.3 Boosted Fashions – Modeltime Workflow

Modeltime – Accuracy Analysis & Figuring out Damaged Fashions (2:43)
Modeltime – Forecast Take a look at Knowledge (2:10)
Modeltime – Refitting & Forecasting Future (3:08)
Save Your Work (1:26)

12.4 Code Checkpoint – Boosted Algorithms

Code Checkpoint (File Obtain)

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 Fashions (It is Straightforward with Modeltime)

Combining Our Artifacts – 28 Fashions! ? (3:06)
Accuracy Evaluation & Hyperparameter Tuning Candidate Choice (This Used to Take Me Weeks To Do) (4:36)

13.2 [SEQUENTIAL MODELS] NNETAR – Hyperparameter Tuning Course of

What are Sequential Fashions? (& Why do we have to tune them in another way?) (2:55)
Extracting the Workflow from a Modeltime Desk: pluck_modeltime_model() (1:40)
Time Series Cross Validation (TSCV) Specification, Half 1: time_series_cv() (4:34)
Time Series Cross Validation (TSCV), Half 2: plot_time_series_cv_plan() (4:14)
Establish Tuning Parameters – Recipe Spec (3:07)
Establish Tuning Parameters – Mannequin Spec (5:14)
Make a Grid for Parameters – Grid Spec (5:55)

13.2.1 – NNETAR Tuning, Spherical 1 – Default Params

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

13.2.2 NNETAR Tuning, Spherical 2 – Discovering the Candy Spot!

Replace Grid Parameter Ranges (8:13)
Parallel Processing – Pace-Up Tuning (5:13)
Pace Comparability (Parallel vs Series) – 3.4X Pace Increase (44 sec vs 151 sec)
Evaluation Parameters vs Performance Metrics (1:09)
NNETAR – Prepare the Last Mannequin – Finest RMSE 0.507 ? (4:15)

13.3 [NON-SEQUENTIAL MODELS] Prophet Increase – Hyperparameter Tuning Course of

What are Non-Sequential Fashions? (2:44)
Mannequin Extraction: pluck_modeltime_model() (1:04)
Ok-Fold Cross Validation (Use with Non-Sequential Fashions ONLY) (4:23)
Prophet Increase – Recipe (1:10)
Prophet Increase – Mannequin Spec (Establish Parameters for Tuning) (3:57)

13.3.1 Prophet Increase Tuning, Spherical 1 – Default Parameters

Grid Specification – Grid Latin Hypercube w/ Default Parameters (4:52)
Tuning the Grid (in Parallel) (6:18)
Visualize Outcomes – Studying Charge Dominates ⚡ (2:58)

13.3.2 Prophet Increase Tuning, Spherical 2 – Controlling Studying Charge

Grid Specification – Controlling Studying Charge (4:45)
Hyperparameter Tuning – Spherical 2 – We will see parameter tendencies! ? (3:17)

13.3.3 Prophet Increase Tuning, Spherical 3 – Honing In

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

13.4 Saving Our Progress

Recap & Saving the Fashions (6:53)

13.5 Code Checkpoint – Mannequin 13 – Hyperparameter Tuning

Code Checkpoint (File Obtain)

14.0 Ensemble Time Series Fashions (Stacking)

Competitors Ensembling Evaluation (5:57)
What’s an Ensemble Mannequin? (7:21)
Modeltime Ensemble: Documentation (2:01)
Forecasting Cheat Sheet Improve ?️ [Download Here] (1:00)

14.1 Mannequin Performance Evaluation

Code Setup [File Download] (6:49)
Reviewing Fashions – Combining Tables & Organizing Outcomes (4:24)
Reviewing Fashions – Making Sub-Mannequin Choices (7:46)

14.2 Common Ensemble

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

14.3 Weighted Common Ensembles

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

14.4.A Stacked Ensembles – Stacking Course of

Introduction to Meta-Learner Ensembling with Modeltime Ensemble (3:57)
Resampling: Time Series Cross Validation (TSCV) Technique (5:17)
Making Sub-Mannequin CV Predictions – modeltime_fit_resamples() (4:27)
Resampling & Sub-Mannequin Prediction: Ok-Fold Technique (6:28)
Linear Regression Stack – TSCV – RMSE 1.00 (Ouch!) ? (7:16)
Linear Regression Stack – Ok-Fold – RMSE 0.651 (A lot Higher, however We Can Do Higher) ? (3:25)

14.4.B Stacked Ensembles – Stacking with Tunable Algorithms

GLMNET Stack – RMSE 0.641 (Heading in the right direction) ? (6:38)
Modeltime Ensemble – In-Pattern Prediction Error – Bug Squashed (1:10)
Random Forest Stack – RMSE 0.587!!! (7% enchancment) ?? (4:33)
Neural Internet 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-Degree Stacking

Degree 2 – Mannequin Analysis & Choice (4:27)
Degree 3 – Weighted Ensemble Creation, Analysis, & Choice – RMSE 0.595 (Degree 2 RF is New Baseline RMSE 0.585) (3:34)

14.6 Modeltime Workflow for Ensembles

Ensemble Calibration (4:45)
Ensemble Refitting, Technique 1: Retraining Submodels Solely (5:43)
Ensemble Refitting, Technique 2: Retraining each Sub-Fashions & Tremendous-Learners (5:33)

14.7 Saving Your Work

Save the Multi-Degree Ensemble (1:27)
Object Measurement: 50MB! Here is why. ? (3:15)

14.8 Code Checkpoint – Module 14 – Ensemble Strategies

Code Checkpoint [File Download]

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

Welcome to Module 15 – Forecasting at Scale utilizing Panel Knowledge (Non-Recursive) Methods (2:30)
Setup [File Download] (4:30)

15.1 Knowledge Understanding & Preparation

Knowledge Understanding (4:33)
Knowledge Prep, Half 1: Padding by Group | Ungrouped Log Transformation (3:53)
Knowledge Prep, Half 2: Lengthen by Group (2:44)
Knowledge Prep, Half 3: Fourier Options & Lag Options by Group (6:03)
Knowledge Prep, Half 4: Rolling Options by Group | Including a Row ID (4:59)
Future & Ready Knowledge – Preparation (7:34)

15.2 Time Splitting – Prepare/Take a look at

Time Series Cut up (Prepare/Take a look at) (3:50)

15.3 Preprocessing & Recipes

Cleansing Outliers by Group (5:18)
Recipe, Half 1: Time Series Calendar Options (3:24)
Recipe, Half 2: Normalization (Standardization) & Categorical Encoding (5:36)

15.4 Modeling: Make 7 Panel Fashions

Panel Mannequin 1: Prophet with Regressors (2:11)
Panel Mannequin 2: XGBoost (2:41)
Panel Mannequin 3: Prophet Increase (1:57)
Panel Mannequin 4: SVM (Radial) (2:02)
Panel Mannequin 5: Random Forest (1:31)
Panel Mannequin 6: Neural Internet (1:27)
Panel Mannequin 7: MARS (1:27)
Accuracy Verify – This may assist us choose fashions for tuning (3:22)

15.5 Hyperparameter Tuning the Panel Fashions

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

15.6 Modeltime Panel Analysis

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

15.7 Time Series Cross Validation (Modeltime Resample)

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

15.8 Ensemble Panel Fashions

Ensemble Common (Imply) & Sub-Mannequin Choice (2:47)
Accuracy (Take a look at Set, No Inversion) (1:18)
Forecast Visualization (Take a look at Set, Inverted) (3:57)
Accuracy by Group (Take a look at Set, Inverted): summarize_accuracy_metrics() [MAE 46 ?] (4:29)
Refitted Ensemble & Future Forecast (6:11)
Ensemble Median: Keep away from Overfitting (3:29)

15.9 Recap

? Congrats – You Simply Forecasted 20 Time Series Utilizing Panel Knowledge Strategies! (2:28)

15.10 Code Checkpoint – Panel Knowledge

Code Checkpoint [File Download]

✨ [PART 3] Deep Studying with GluonTS

Welcome to Half 3 – Deep Studying with GluonTS (0:53)
RStudio IDE Preview Model | Finest for Working with Python

Module 16 – Setting Up GluonTS & Intro to Reticulate

What’s a Python Setting? And, why do I would like it?
Setup [File Download] (1:19)
R Package deal Set up Necessities (2:30)

16.1 Configuring the “r-gluonts” Python Setting

GluonTS Setting Setup Overview (2:10)
Putting in the Python “r-gluonts” Setting (2:15)
Connecting to the “r-gluonts” Setting (2:48)
Troubleshooting Set up (2:50)

16.2 Testing Modeltime GluonTS ?

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

16.3 Attending to Know Reticulate & Your Python Environments ?

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

16.4 Utilizing a Customized GluonTS Setting

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

16.5 Code Checkpoint – GluonTS Setting Setup

Code Checkpoint [File Download]

Module 17 – Utilizing GluonTS with Reticulate

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

17.1 Reticulated Python Fundamentals

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

17.2 Knowledge, Preprocessing, & GluonTS ListDatasets

Getting the Weekly Transactions Knowledge (1:35)
Making ready the Full Knowledge for Deep Studying (4:36)
Making a GluonTS ListDataset from a Knowledge Body (Tibble) (3:10)
Analyzing a GluonTS ListDataset (5:33)
Changing from GluonTS ListDataset to Pandas Series (7:20)

17.3 DeepAR GluonTS Mannequin

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

17.4 Visualizing the Forecast | matplotlib, ggplot, & plotly

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

17.5 Introducing Modeltime GluonTS

Modeltime DeepAR | Workflow Advantages (6:56)
Modeltime DeepAR | Including Extra Epochs (1:17)

17.6 Saving & Loading GluonTS Fashions

Save & Load | Utilizing GluonTS & Reticulate (6:06)
Save & Load | Modeltime GluonTS Fashions (3:28)

17.7 ?Bonus!! GluonTS Deep Issue Fashions

Making a DeepFactorEstimator (5:11)
Visualizing the Deep Issue Predictions with Matplotlib (3:17)

17.8 Conclusions & Professional/Cons

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

17.9 Code Checkpoint – GluonTS Reticulate

Code Checkpoint [File Download]

Module 18 – Time Series Teams with Modeltime GluonTS

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

18.1 Knowledge Assortment & Preliminary Preparation

Getting the Knowledge | GA Webpage Visits Each day (2:17)
Full Knowledge | Padding the Knowledge (4:02)
Different Padding Technique
Full Knowledge | Log1P Transformation (Goal) (1:01)
Full Knowledge | Lengthen (Future Body) (1:41)
Full Knowledge | Group-Clever Fourier Series (2:33)
Full Knowledge | Group-Clever Including Lagged Options (1:47)
Full Knowledge | Group-Clever Rolling Options (3:10)
Full Knowledge | Including a Row ID (0:52)
Knowledge Ready | skimr::skim() – Be careful for lacking information (2:11)
Future Knowledge | skimr::skim() – Be careful for lacking information (4:07)
Cut up Knowledge Ready (Prepare/Take a look at) (2:15)
Visually Examine the Prepare/Take a look at Splits – Examine for lacking teams (3:37)

18.2 Deep Studying Fashions – DeepAR

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

18.3 Deep Studying Fashions – N-BEATS

N-BEATS (Mannequin 4) | Understanding nbeats() & Coaching Our 1st N-BEATS Mannequin (9:57)
N-BEATS (Mannequin 5) | Enhancing our mannequin with a brand new loss_function (MAE 0.611) (4:25)
N-BEATS (Mannequin 6) | Ensemble A number of N-BEATS (7:09)
N-Beats (Mannequin 6) | Mannequin Accuracy (MAE: 0.544) (3:04)
Future Forecast | Examine Refitted Fashions (6:01)

18.4 Machine Studying – XGBoost

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

18.5 Evaluating Our ML & DL Fashions

Consider 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 Fashions [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 Fashions & Conclusions

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

CONGRATULATIONS!!! You. Did. It.

WOO HOO!!! Get YOUR Certificates & a reduction in your subsequent buy! (1:07)

✨SPECIAL Modeltime Ecosystem: Bonus Classes

Preview

In regards to the Particular Bonus Classes
Hierarchical Forecasting with Modeltime (105:37)
Modeltime H2O: Forecasting with H2O AutoML (63:43)
Modeltime Recursive: Autoregressive Forecasting (Lags < Forecast Horizon) | Power Demand (95:16)
Forecasting Airline Passengers Covid-19 | Modeltime 0.7.0 Updates | PyTorch, GluonTS, International Baselines (93:34)
How you can Forecast 100 Time Series | Modeltime Nested (Iterative) Forecasting (113:01)

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Duration 10 weeks
Skill level All levels
Language English
Students 208
Assessments Yes

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