Industrial Forecasting with Foundation Models
Most operations have an abundance of signals but a shortage of clean data. This course teaches you to anticipate failures and asset behavior with foundation time-series models — even without the perfect dataset that will never arrive on time.
Abundance of signals.
Shortage of clean failure labels.
Most industrial operations have historians full of signals: temperature, vibration, pressure, current, cycles. The problem is not the volume of data — it is that most of those signals carry no reliable failure labels, events of interest are rare, and the datasets that do exist are heterogeneous, with gaps, sensor changes, and periods of abnormal operation that no one documented. Waiting for the perfect dataset delays an operation's predictive capability by years, not months.
Classical machine-learning approaches for time series — ARIMA, statistical models, recurrent networks trained from scratch — require exactly what most operations do not have: clean, sufficient, and consistent data. Foundation time-series models change that equation. Amazon Chronos, Google TimesFM, and equivalent approaches are pretrained models capable of making reasonable predictions in zero-shot or few-shot mode — without an extensive history, without perfect labels, without months of training.
But applying them correctly in an industrial context requires understanding their limits: when they make sense, what metrics matter for operations, how to prepare multivariate signals with gaps and drift, and how to integrate predictions into real maintenance decisions. This course is designed for that context: real assets, noisy signals, and organizations in regulated markets that cannot wait years to capture predictive value.
What you will build
Four modules spanning the fundamentals of new forecasting models through integration with real maintenance and operational decisions.
From classical per-asset models to models pretrained on millions of series. What Amazon Chronos, Google TimesFM, and equivalent approaches are. When they make sense in an industrial context and when classical approaches remain competitive. Conceptual architecture of foundation time-series models.
3 hoursMultivariate series, sampling frequency, time windows, and signal quality. Handling missing values, sensor drift, and rare events. Relevant contextual variables in industrial assets: operating cycles, scheduled maintenance, environmental conditions. When to normalize and when not to.
4 hoursZero-shot forecasting: applying pretrained models without your own training data. Few-shot: how many examples are enough. Benchmark against ARIMA, Prophet, and classical statistical methods on real industrial signals. Metrics that actually matter in operations: not generic MAE, but anticipation error and false alarm rate.
4 hoursEarly deviation detection: how to turn a prediction into an actionable alert. Integration with monitoring systems, maintenance planning, and work orders. Limits of the approach and criteria for knowing when a predictive model is ready for production. Analytical maturity roadmap for industrial assets.
3 hoursWhat you will be able to do
Apply foundation time-series models to your own industrial signals — with incomplete, heterogeneous data and without a perfect history — and evaluate results with metrics that matter in operations.
Turn asset behavior predictions into actionable alerts for predictive maintenance. Define anticipation thresholds relevant to the process — not just statistical metrics without operational context.
Concrete criteria to evaluate whether a predictive model is ready to integrate into real operational decisions — and an analytical maturity roadmap for expanding coverage to multiple assets without losing precision.
Who teaches this course
Seven years leading data ecosystems at BCI, work at Canadian ad-tech firm illumin, with stints at the IDB, PDVSA, Falabella, and Walmart across five countries. Founded Yaripo with the mission of closing the gap between AI strategy and real implementation at mid-size organizations with critical operations.
Yaripo designs this course from the opposite bias to the academic: noisy signals, imperfect histories, and organizations in regulated markets that cannot wait two years to build the ideal dataset before capturing predictive value. While most forecasting training offerings work with clean, well-labeled benchmark datasets, Yaripo grounds the content in real assets — with sensor drift, rare events, data gaps, and integration constraints imposed by existing maintenance systems.
What engineers ask before enrolling
Your assets are already talking.
This course teaches you to listen before the failure.
14 hours. 4 modules. A working forecasting prototype built on real signals from your operation — without waiting for the perfect dataset.
Enrollment at academia.yaripo.cl · Online asynchronous format · SENCE tax-credit compatible