What You’ll Uncover in High Performance Time Series
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 methods to scale back forecast error utilizing state-of-the-art forecasting methods which have gained competitions.
High Performance Time Series
High Performance Time Series
Turn out to be the time-series area skilled on your group
Turn out to be the Time Series Professional
on your group
The High-Performance Time Series Forecasting Course is an incredible course designed to show Enterprise Analysts and Knowledge Scientists methods to scale back forecast error utilizing state-of-the-art forecasting methods which have gained competitions. You may 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 scale back forecasting error and scale outcomes on your group.
That is presumably my most difficult course ever. You may study the time sequence expertise which have taken me 10-years of examine, apply, and experimentation.
My discuss on High-Performance Time Series Forecasting
This course offers you the instruments it’s essential meet at present’s forecasting calls for.
A full yr was spent on constructing two of the software program packages you will study, modeltime and timetk.
Plus, I am instructing you GluonTS, a state-of-the-art deep studying framework for time sequence written in python.
This course will problem you. It is going to change you. It did me.
– Matt Dancho, Course Teacher & Founding father of Enterprise Science
Endure a Full Transformation
By studying forecasting methods that get outcomes
With High-Performance Forecasting, you’ll endure an entire transformation by studying probably the most in-demand expertise for creating high-accuracy forecasts.
By this course, you’ll study and apply:
- Machine Studying & Deep Studying
- Function Engineering
- Visualization & Knowledge Wrangling
- Transformations
- Hyper Parameter Tuning
- Forecasting at Scale (Time Series Teams)
The way it works
Your path to turning into an Professional Forecaster is simplified into 3 streamlined steps.
1
Time Series Function Engineering
2
Machine Studying for Time Series
3
Deep Studying for Time Series
Half 1
Time Series Function Engineering
First, we construct your time sequence characteristic engineering expertise. You study:
- Visualization: Figuring out options visually utilizing the simplest plotting methods
- Knowledge Wrangling: Aggregating, padding, cleansing, and increasing time sequence information
- Transformations: Rolling, Lagging, Differencing, Creating Fourier Series, and extra
- Function Engineering: Over 3-hours of content material on introductory and superior characteristic engineering
Half 2
Machine Studying for Time Series
Subsequent, we construct your time sequence machine studying expertise. You study:
- 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 Enhance & ARIMA Enhance
- Hyper Parameter Tuning: Methods to scale back overfitting & improve mannequin efficiency
- Time Series Teams: Scale your evaluation from one time sequence 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 sequence deep studying expertise. You study:
- GluonTS: A state-of-the-art forecasting bundle that is constructed on high of mxnet (made by Amazon)
- Algorithms: Be taught DeepAR, DeepVAR, NBEATS, and extra!
Challenges & Cheat Sheets
Subsequent, we construct your time sequence machine studying expertise. You study:
- Cheat Sheets: Developed to make your forecasting workflow reproducible on any drawback
- Challenges: Designed to check your skills & solidify your data
Abstract of what you get
- A methodical coaching plan that goes from idea to manufacturing ($10,000 worth)
- Half 1 – Function 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 normal enterprise & finance guru, He has labored with many purchasers from Fortune 500 to high-octane startups! Matt loves educating information scientists on methods to apply highly effective instruments inside their group to yield ROI. Matt would not relaxation till he will get outcomes (actually, he would 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 Professional for Your Group (2:34)
- Personal Slack Channel – The way to Be part of
- Video Subtitles (Captions)
- What’s a High-Performance Forecasting System?
- [IMPORTANT] System Necessities – R + Python Necessities & Widespread 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 Overview
- The Forecasting Competitors Overview & Course Development (3:34)
- 2014 Kaggle Walmart Recruiting Problem (5:11)
- 2018 M4 Competitors (3:37)
- 2018 Kaggle Wikipedia Web site Visitors Forecasting Competitors (4:30)
- 2020 M5 Competitors (5:59)
- 5 Key Takeaways from the Forecast Competitors Overview (5:41)
0.2 Course Initiatives – Google Analytics, Electronic 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 Challenge Setup
- Challenge Setup (2:28)
- Course Knowledge (File Obtain) (1:02)
- R Bundle Set up – Half 1 (File Obtain) (5:26)
- R Bundle 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 Prime 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 Electronic 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 & Practice/Check Home windows
- Analysis Window – Filter By Time (4:43)
- Time Series Practice/Check 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 Function Engineering
- Visualizing Seasonality (4:34)
- Function Engineering – Half 1 (5:45)
- Function 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 Broad-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 & Net Visitors (3:48)
- Inspecting the Be part of (3:00)
- Formatting the Be part of for Function 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 Shifting Common? (File Obtain) (3:53)
- Rolling Home windows – Shifting 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 Drawback with Forecasting utilizing a Shifting 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)
- Getting 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 & Net Web page 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 Function Engineering (for Time Series)
- Setup (File Obtain) & Overview – Intro to Function 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-Based mostly Options (Development & Seasonal/Calendar) [MUST KNOW]
- The Time Series Signature (7:55)
- Function Elimination (3:28)
- Linear Development (2:10)
- Non-Linear Development – Foundation Splines (4:41)
- Non-Linear Development – Pure Splines (Stiffer than Foundation Splines) (4:29)
- Seasonal Options – Weekday & Month (3:21)
- Seasonal Options – Combining with Development (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
- Getting 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)
- Reworking 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 Beneficial Mannequin Options
- Amassing the Beneficial Mannequin (3:44)
- Saving the Mannequin Artifact (2:28)
5.8 Code Checkpoint – Module 05 – Introduction to Function Engineering
- Code Checkpoint (File Obtain)
Module 06 – Superior Function Engineering Workflow
- Forecasting Workflow [CHEAT SHEET] ️ (3:40)
- Setup (File Obtain) & Overview – Superior Function 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
- Practice / Check Cut up (3:55)
6.4 Recipes – Function Engineering Pipeline Steps
- Recipes Intro (2:41)
- Step – Time Series Signature Options (5:48)
- Step – Function Elimination (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 Check Knowledge (2:40)
- Measuring the Check 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 Record, Half 1 (4:34)
- Creating an Artifact Record, Half 2 (3:11)
- Organizing the Artifacts Record (1:57)
- Saving the Artifacts (1:28)
6.10 Code Checkpoint – Module 06 – Superior Function Engineering
- Code Checkpoint (File Obtain)
⛰️ Problem #2 – Function Engineering & Modeltime Workflow [YOU’VE GOT THIS!]
- Problem Dialogue, Half 1 (File Obtain) – Function Preparation (5:11)
- Problem Dialogue, Half 2 – Function Engineering & Modeling (4:56)
Problem #2 – Answer
- Answer, Half 1 (File Obtain) – Gather & 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 – Practice/Check Knowledge Cut up (0:56)
- Answer, Half 6 – Function 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 – Bettering 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
- Selecting Up From Half 1 (Challenge 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-Based mostly 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!!!]
- Main 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 Check 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 Capabilities: 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)
- Shifting 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 Totally different ARIMA Parameters (5:11)
- About AIC (Akaike Info 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 Choose 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 Improve – 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 Operate (4:50)
- Making use of the Exponential Weighting Operate 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
- Getting ready the Check 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) – via 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 – Practice/Check 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 Overview (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 Overview (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 Overview (1:30)
- Answer, Half 15 – Forecasting the Future Knowledge – ARIMA, Prophet & ETS/TBATS (3:40)
- Answer, Half 16 – Closing Overview – 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 operate 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 Help 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 Help Vector Machine (SMV) – Radial Foundation Operate
- 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-Based mostly Algorithms – The Drawback with Predicting Time Series Tendencies
- Strengths/Weak spot – KNN & Tree-Based mostly Algorithms Cannot Predict Past the Min/Max (1:24)
- KNN vs GLMNET – Making Pattern Knowledge with Development (2:08)
- KNN vs GLMNET – Making Easy Development Fashions (4:12)
- KNN vs GLMNET – Visualize the Development Predictions w/ Modeltime – Yikes, GLMNET simply schooled KNN (4:14)
11.5 Okay-Nearest Neighbors (KNN) – Similarity (Distance) Based mostly
- 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-Based mostly)
- 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-Based mostly
- 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 Bushes (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 Overview
- Organizing in a Modeltime Desk (4:22)
- Updating the Descriptions Programmatically (4:02)
- Mannequin Choice – Course of & Ideas (utilizing Accuracy Desk) (3:39)
- Mannequin Inspection – Course of & Ideas (utilizing Check 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() operate (1:29)
11.13 Checkpoint – Module 11 – Machine Studying Algorithms
- Code Checkpoint (File Obtain)
12.0 Boosted Algorithms – Prophet Enhance & ARIMA Enhance
- Boosted Algorithms – A Highly effective Approach for Bettering Performance (3:37)
12.1A Prophet Baseline Mannequin
- Baseline: Finest Prophet Mannequin (2:38)
- [Pro Tip] The way to Repair a Damaged Mannequin (2:50)
- Prophet Baseline – Finest Mannequin MAE 0.488 (0:54)
12.1B Prophet Enhance
- Recipe for Prophet Enhance (3:33)
- Mannequin Technique – Utilizing XGBOOST for Seasonality/XREG Modeling (4:39)
- Workflow – No Parameter Tweaking (3:41)
- [KEY CONCEPT] Prophet Enhance – Modeling Development with Prophet, Residuals with XGBoost (3:00)
- Prophet Enhance – Tweaking Parameters – BEST MAE 0.457 (6:33)
12.2 ARIMA Enhance
- Modeling Technique – ARIMA for pattern, XGBOOST for XREGS (3:50)
- ARIMA Enhance – Mannequin Specification (5:57)
- ARIMA Enhance – 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 Check 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 Overview & 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)
- Determine Tuning Parameters – Recipe Spec (3:07)
- Determine 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 – Velocity-Up Tuning (5:13)
- Velocity Comparability (Parallel vs Series) – 3.4X Velocity Enhance (44 sec vs 151 sec)
- Overview Parameters vs Performance Metrics (1:09)
- NNETAR – Practice the Closing Mannequin – Finest RMSE 0.507 (4:15)
13.3 [NON-SEQUENTIAL MODELS] Prophet Enhance – Hyperparameter Tuning Course of
- What are Non-Sequential Fashions? (2:44)
- Mannequin Extraction: pluck_modeltime_model() (1:04)
- Okay-Fold Cross Validation (Use with Non-Sequential Fashions ONLY) (4:23)
- Prophet Enhance – Recipe (1:10)
- Prophet Enhance – Mannequin Spec (Determine Parameters for Tuning) (3:57)
13.3.1 Prophet Enhance 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 Enhance Tuning, Spherical 2 – Controlling Studying Charge
- Grid Specification – Controlling Studying Charge (4:45)
- Hyperparameter Tuning – Spherical 2 – We will see parameter traits! (3:17)
13.3.3 Prophet Enhance 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 Overview (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 Overview
- 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: Okay-Fold Technique (6:28)
- Linear Regression Stack – TSCV – RMSE 1.00 (Ouch!) (7:16)
- Linear Regression Stack – Okay-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, Methodology 1: Retraining Submodels Solely (5:43)
- Ensemble Refitting, Methodology 2: Retraining each Sub-Fashions & Tremendous-Learners (5:33)
14.7 Saving Your Work
- Save the Multi-Degree Ensemble (1:27)
- Object Dimension: 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: Prolong 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 – Practice/Check
- Time Series Cut up (Practice/Check) (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 Enhance (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 Examine – This can assist us choose fashions for tuning (3:22)
15.5 Hyperparameter Tuning the Panel Fashions
- Tuning Resamples: Okay-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 (Check Set, No Inversion) (1:18)
- Forecast Visualization (Check Set, Inverted) (3:57)
- Accuracy by Group (Check 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 Bundle 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)
- Getting 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 (Execs & 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)
- Various Padding Technique
- Full Knowledge | Log1P Transformation (Goal) (1:01)
- Full Knowledge | Prolong (Future Body) (1:41)
- Full Knowledge | Group-Sensible Fourier Series (2:33)
- Full Knowledge | Group-Sensible Including Lagged Options (1:47)
- Full Knowledge | Group-Sensible 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 (Practice/Check) (2:15)
- Visually Examine the Practice/Check 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) | Bettering 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, World Baselines (93:34)
- The way to Forecast 100 Time Series | Modeltime Nested (Iterative) Forecasting (113:01)
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