Advanced Industrial Specialization

Time Series with
Deep Learning

Master temporal AI for mission-critical systems. Engineering reliability, coherence, and trust into your forecasting pipelines.

Flexible Delivery

Available as Online Live sessions or Frontal on-site training.

Full Video Access

Includes immediate access to the 18-lesson high-intensity recorded course.

Request Course Details →

Among Metaor AI’s clients are some of the leading companies in the industry.

Is this program for you?

This is an advanced track for those who already master Deep Learning basics.

Senior Algorithm Engineers

Professionals building complex systems where time is a first-class citizen-handling causality, feedback loops, and long-term stability.

Staff Data Scientists

Practitioners leading forecasting projects in Retail, Finance, or Energy who need to enforce global coherence and quantify business risk.

Industrial Forecasting Specialization

Available Training Tracks

Format	Intensity & Duration	Key Outcome
Standard Professional Track	5 Weeks (8h / week)	Deep Temporal Architectures and robust validation.
Recommended Deep Dive	10 Weeks (8h / week)	Mastery from Hierarchical Forecasting to Foundation Models.
Recorded Video Course	18 Lessons (On-Demand)	Immediate access to all 18 lessons and Google Colab labs.
Corporate Live / Frontal	Worldwide Delivery	On-site workshops tailored to your organization.

Industrial Specialization Syllabus

Block I: Framing Time as a System

01Time Series Are Systems: Violated IID & Feedback Loops

02Failure Modes: Regime Shifts & Silent Degradation

03Data Pipelines: Temporal Leakage & Window Design

Block II: Modeling Choices That Matter

04Classical Baselines (ARIMA/ETS) as Diagnostic Tools

05Deep Architectures: LSTM, TCN, and Latency Trade-offs

06Modern Backbones: Transformers vs. SSMs (Mamba)

07Hierarchical Forecasting: Coherence Constraints (MinT)

08Spatial–Temporal Dependencies: ST-GNNs for Networks

09Production Trade-offs: Accuracy vs. Cost vs. Coherence

Block III: Foundation Models & Uncertainty

10Foundation Models: Chronos, TimesFM & Lag-LLaMA

11Uncertainty: Quantile Loss & Probabilistic Outputs

12Business Risk: Asymmetric Cost Management (WAPE)

13Trust Boundaries: Confidence Collapse & OOD Signals

Block IV: Validation, Drift, and Control

14Validation: Walk-Forward Backtesting for Stability

15Silent Failure: Data Drift vs. Concept Drift Analysis

16Retraining Control: Scheduled vs. Triggered Updates

17Monitoring: Distribution Health & Stability Metrics

Block V: Industrial Integration

18Capstone: Design & Deployment of an End-to-End System

Engineering Excellence & Deliverables

Systemic Stability

Bridge the gap between point-forecasts and reliable systems. Master temporal leakage prevention, walk-forward validation, and hierarchical coherence constraints.

Advanced Backbones

Go beyond tutorials. Implement State Space Models (Mamba), Temporal Fusion Transformers, and ST-GNNs for networked systems like logistics and power grids.

Industrial Lab Kit

Access ready-to-deploy Google Colab labs. Implement Zero-shot forecasting with Foundation Models (Chronos/TimesFM) and fine-tuning scripts for your specific domain.

Risk & Control

Ensure business continuity through Uncertainty Quantification. Design drift detection control loops and retraining strategies that reflect real asymmetric business risks.

Explore AI Literacy & Leadership Programs

Explore AI Developers Programs

Founder

Dr. Barak Or

A researcher, lecturer, and entrepreneur specializing in Artificial Intelligence. Dr. Or holds three degrees from the Technion and a PhD in machine learning.

As the Academic Director of AI program for Google-Reichman Tech School and Lecturer at Reichman University, he bridges the gap between academic theory and industrial scale. He has authored numerous patents and founded metaor.ai, and other AI startups, and serves as an advisor and lecturer for defense organizations and tech companies in Israel and abroad.