Science of Forecasting Course

Build Your CustomAI Forecast Analyst

Forecast like a data scientist without becoming one.

LLMs have changed everything. You no longer need to know Python to apply the most advanced machine learning models to your own data—and generate the kind of data-driven insights that used to require a PhD. We will show you exactly how to do it.

20+ hours of short, one-topic, on-demand, pre-recorded videos. 5-10 minute lessons. Self-paced with no time limit, no assignments, and forever course access.

Join a community of students from leading global brands

20+ hoursShort, one-topic, on-demand videos

5-10 minutesLessons designed to fit into your lifestyle

Forever accessSelf-paced with no time limit

Real transaction dataWork from practical business examples

Unlimited course updatesEvery new module and improvement included for life

Who has registered for this course?

Built for professionals who need to predict revenue, demand, and business performance.

ControllersAccounting ProsFinancial ProsSales ManagersConsultantsRevenue ProsBookkeepersTax Pros

What You Will Learn

The forecasting methods that actually move the needle—now accessible to everyone.

Statistical and Machine Learning Methods

Statistical Concepts for Forecasting
Moving Averages and Exponential Smoothing
Seasonality and Trend Decomposition
Regression Techniques
Bayesian Forecasting
The pattern-detection workhorse (ARIMA / SARIMA)
The model that handles seasonality automatically (Prophet)
The algorithm that outperforms almost everything (XGBoost)
Hundreds of models voting on your forecast (Random Forest)
Deep learning that reads sequences and memory (LSTM)
Finding what variables actually move your numbers (Driver ML)
Measuring and Improving Forecast Accuracy

Applied to Real Business Data

Forecasting Year-over-Year Growth
Forecasting Techniques for Contract Sales
Forecast Customer Pickup and Attrition
Forecast Sales Funnels Performance
Forecast Wholesale Sales
Dynamic Forecasting for Individual Sales
Forecast Bundled Sales
Forecast Variable Costs
Forecast the 21 Dimensions of Revenue
Building a P&L Forecasting Model
Build Rolling Forecasts
Connecting to Transaction Systems for Streaming Forecasts

All the skills needed to be a Forecasting Expert

Master every forecasting method—then run the advanced ones with AI.

Getting Your Data Ready

A forecast is only as good as the data behind it. Before any model runs, you need clean, structured, and well-labeled data. This module covers how to extract business data from any source, clean it up, and organize it in a way that forecasting models can actually use. AI handles the technical execution—your job is to understand what the data represents and what you are asking it to predict.

You will learn to prepare real business data for forecasting, build a data ontology that helps LLMs understand your business context, and avoid the data quality mistakes that silently ruin most forecasts.

From Spreadsheet to AI Model

Most professionals already have forecasting models built in spreadsheets—formulas, pivot tables, and logic that took real time to build. This module shows you how to take exactly what you already have and hand it to an AI to replicate, extend, and automate. You do not start from zero. You start from where you already are.

You will take a real spreadsheet-based forecast, walk an AI through its logic, and emerge with an AI-powered version that updates itself with new data, flags its own errors, and can be extended into every advanced algorithm covered in the rest of the course.

Part 1Mathematical Forecasting with AI

The foundational methods every serious forecaster must understand. You will learn the statistical and mathematical models that have underpinned business forecasting for decades—now executed entirely with AI assistance so you can focus on the results, not the code.

The Statistics Every Forecaster Needs to Know

You do not need a statistics degree to forecast well. But you do need to understand a handful of core ideas—what a distribution is, why correlation is not causation, what variance tells you about your data, and how time series data behaves differently from a simple table of numbers. This module gives you the statistical foundation that makes everything else in the course make sense.

You will learn to read and interpret the statistical outputs that every forecasting model produces, so you can evaluate results intelligently rather than just trusting a number the model hands you.

What Is a Forecasting Algorithm? (In Plain English)

An algorithm is a set of rules a computer follows to find patterns in your data and use them to predict what comes next. That is it. Different algorithms were invented for different types of data behavior—some are built for data that repeats in cycles, others for data with complex cause-and-effect relationships, others for sequences where the distant past influences the future in subtle ways. Picking the wrong algorithm is one of the most common forecasting mistakes.

You will learn to look at a business forecasting problem and immediately recognize which class of algorithm belongs on it—before touching any data or writing any code.

Averaging Out the Noise (Moving Averages)

The simplest forecasting insight is that the average of recent data is usually a better prediction of tomorrow than yesterday’s single data point. Moving averages smooth out the day-to-day noise in your business numbers so the real underlying trend becomes visible. Simple moving averages treat all recent periods equally. Weighted moving averages let you tell the model that recent data matters more than older data—which is almost always true in business.

You will calculate and interpret both types of moving average on real business data, choose the right lookback window for your use case, and understand when these simple techniques outperform far more complex ones.

Forecasts That Learn and Adjust Themselves (Smoothing Methods)

Smoothing models are moving averages that update automatically every time new data arrives. They observe how wrong the last forecast was and adjust their behavior accordingly—without anyone needing to rebuild the model. More advanced versions extend this to simultaneously track and adjust for a trending direction and repeating seasonal cycles. For most real business data that slowly changes over time, these methods are remarkably effective.

You will build smoothing models that handle level, trend, and seasonality in one unified framework, and understand intuitively why they often outperform both simple averages and more complex algorithms on everyday business data.

Forecasting Under Uncertainty (Bayesian Methods)

Every other forecasting method gives you a single number as its answer. Bayesian methods give you a range of possibilities and tell you how confident the model is in each one. They also let you incorporate what you already know about your business before the data even speaks. This is how professional risk analysts and scenario planners think about the future—not as a single point, but as a distribution of outcomes.

You will understand how to produce probability-based forecasts, incorporate expert judgment into a model without overriding the data, and communicate forecast uncertainty in terms executives can act on.

How One Variable Predicts Another (Regression)

Regression is the most widely used forecasting tool in business finance and economics. It answers a specific question: if this driver changes, how much does my outcome change? Revenue per salesperson, price elasticity, cost per unit at scale—these are all regression relationships. Understanding regression is the foundation for understanding every machine learning model that follows.

You will learn to identify driver variables, build regression models on real business data, and interpret the results in plain English—understanding not just what the model predicts, but why.

Part 2Algorithmic Forecasting with AI

The machine learning algorithms that were previously accessible only to engineers and PhDs. You will run every major model on real business data using Claude or Codex as your technical copilot—no programming experience required.

The Pattern-Detection Workhorse (ARIMA & SARIMA)

These are the models that statisticians and quantitative analysts have used for decades to find hidden patterns in time series data. They identify cycles, trends, and seasonal rhythms that the human eye misses entirely. Until recently, running them required fluency in Python or R. Now Claude or Codex writes, runs, and explains the code while you focus on interpreting results and applying business judgment.

You will implement these models on your own business data, tune the parameters, and translate the output into clear management forecasts—without writing a single line of code yourself.

The Model That Handles Seasonality Automatically (Prophet)

Built by Meta’s data science team and used across some of the largest companies in the world, Prophet was designed specifically for business forecasting. Tell it about your holidays, your seasonal patterns, and your expected trend, and it handles the rest. With an AI copilot executing the code, every analyst now has access to a tool that was previously limited to engineering teams.

You will build Prophet models on real data, adjust for business-specific seasonality and events, and produce scenario-based forecasts ready for executive presentation.

The Algorithm That Outperforms Almost Everything (XGBoost)

XGBoost and its close relatives have won more machine learning competitions than any other algorithm family and are deployed at scale inside Amazon, Uber, and most major financial institutions. When your business drivers are complex and nonlinear, these models see relationships that simpler methods miss entirely. You do not need to understand the math to use them correctly.

You will use Claude or Codex to build these models against real business datasets, evaluate their predictions, and diagnose which variables are actually driving your forecast outcomes.

Hundreds of Models Voting on Your Forecast (Random Forest)

Instead of building one model and hoping it is right, Random Forest builds hundreds of them simultaneously and lets them vote on the answer. The result is a forecast that is far more robust to outliers and noise in real business data. It also tells you, in plain terms, which variables in your data are actually doing the predictive work.

You will run Random Forest models using AI, identify the most influential predictors in your data, and build interpretable forecasts that you can explain to non-technical stakeholders.

Deep Learning That Remembers (LSTM Networks)

Most forecasting models treat each time period as independent. LSTM networks are different—they have memory. They learn which past patterns matter, how far back to look, and which sequences predict what comes next. They are the same class of model used in financial market prediction and demand forecasting at the largest technology companies. Running them no longer requires a machine learning engineering background.

You will implement LSTM models on real business time series data guided entirely by Claude or Codex, and compare their performance against the classical methods covered earlier in the course.

Finding What Actually Moves Your Numbers (Driver-Based ML)

The most powerful forecasts do not just extrapolate the past. They identify the specific variables that cause your business outcomes to move, and model exactly how changes in those drivers ripple through to your forecast. Machine learning is now the most systematic way to find those drivers inside your own data—without guessing.

You will build driver-based models with AI handling all implementation, so you can focus on the business question: what variables actually predict your results, and by how much.

Teaching AI to Understand Your Business (Forecasting Ontology)

An ontology is a structured description of your business data—what each variable means, how your entities relate to one another, and what context an AI needs to reason accurately about your specific operation. Without this, AI tools produce generic outputs. With it, they produce analysis that reflects your actual business logic. A well-designed ontology makes every AI-assisted forecast faster, more accurate, and more explainable.

You will build a practical business ontology for your own organization, enabling Claude and other LLMs to understand your data at a depth that dramatically improves the quality of every AI-generated analysis you run.

Knowing When Your Forecast Is Wrong (and Fixing It)

A forecast without a measure of its own accuracy is just a guess in a spreadsheet. This final module covers the standard metrics professional forecasters use—MAE, RMSE, MAPE—explained in plain English, not statistical notation. More importantly, it covers how to diagnose why a forecast is wrong and apply targeted fixes. A systematic error is always fixable once you know what is causing it.

You will calculate accuracy metrics on your own forecasts, distinguish random error from systematic bias, and build a continuous improvement process that makes your forecasts more reliable with every new cycle of data.

Dozens of Real-World Case Studies

Build forecasts for real business situations.

Forecasting Year-over-Year Growth

Forecasting year-over-year growth is crucial for businesses to anticipate and plan for future revenue performance. This case study focuses on understanding historical trends, identifying factors influencing growth, and developing forecasting models that project revenue growth for the upcoming periods.

You will learn how to predict revenue growth accurately, enabling effective resource allocation and strategic decision-making.

Dynamic Pricing and Forecasting Sales

This case study emphasizes dynamic forecasting techniques tailored to product sales, considering factors such as market demand, pricing strategies, and purchase patterns.

You will learn how to develop flexible and responsive forecasting models, enabling businesses to maximize revenue potential, optimize pricing decisions, and efficiently manage inventory.

Building a Forecasting Model

This case study focuses on the practical aspects of building a forecasting model specifically tailored for revenue and expense forecasting. Students learn the step-by-step process of constructing a forecasting model, including data collection, preprocessing, feature selection, model selection, and model evaluation.

You will learn the skills to develop customized forecasting models that accurately predict revenue, enabling informed decision-making and effective profit management.

Build Rolling Forecasts

Rolling forecasts allow analysts to continuously update and refine their forecasts based on the most recent data and market conditions. This case study focuses on the concept of rolling forecasts and the techniques used to build and maintain them.

You will learn how to incorporate new data, adjust forecast models, and analyze forecast variances to ensure the accuracy and relevance of rolling forecasts.

Forecast Automation

Automating the forecasting process can significantly enhance efficiency and accuracy in forecasting. This exercise explores techniques and tools for automating the forecasting workflow, including data extraction, transformation, model selection, and report generation.

You will gain hands-on experience with forecasting models and learn how to leverage automation to streamline the forecasting process, reduce manual effort, and improve overall forecasting quality.

No-nonsense, Immediate-Impact Training

The models do not change. Only who can use them just did.

ARIMA, Prophet, XGBoost, Random Forest, LSTM—these are the same models used by quantitative analysts, data science teams, and PhD researchers. They have not been simplified for this course. The underlying mathematics is unchanged.

What has changed is who can run them. With Claude and Codex as your technical copilot, the barrier between knowing a method and executing it has collapsed. This course teaches you to use every major forecasting model against real data, without writing code from scratch.

Insights that once required a PhD. Now available to any serious analyst.

Enrollment

Learn forecasting as a serious business analytics discipline.

100% Money-back Guarantee.
Real business data, reusable forecasting models, templates, and Certificate of Completion.
Personal coaching on applying every technique to your own data.
Lifetime access and all future course updates.

Meet Your Instructor

Robert Hernandez

I am Robert Hernandez, and I am committed to helping you learn everything I know, without wasting your time on purely academic skills that do not translate into real-world value. I bring a rare combination of cross-disciplinary experience, advanced mathematical and analytics skills, technical competency, multi-industry knowledge, and business process improvement expertise. I am an energetic and dedicated analytics professional with strong business acumen and more than 20 years of experience creating and managing data-driven models and processes for diverse organizations, both on-site and remotely. I have worked at Walt Disney and HP/Compaq, and have consulted for dozens of companies across industries, from e-commerce to entertainment to energy.

in Connect with Robert on LinkedIn

Have a Question about this course? Email me.

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