Time-Series Forecasting with Machine Learning Certification

The Time-Series Forecasting with Machine Learning Certification is a free intermediate-level, 20-minute skill test from Science of Apps. It tests time-series forecasting with machine learning — trend, seasonality and stationarity, decomposition, autocorrelation and lag features, baselines such as naive and seasonal naive, exponential smoothing and ARIMA, gradient-boosted models with lag and calendar features, deep learning models and foundation models for forecasting, time-based cross-validation and avoiding leakage, error metrics such as MAE, RMSE, MAPE and MASE, probabilistic forecasts and prediction intervals, hierarchical forecasting and reconciliation, demand forecasting with promotions and holidays, and monitoring for drift — written to Hyndman and Athanasopoulos' Forecasting: Principles and Practice and scikit-learn guidance for 2026 and 2027. Score 70% or more on its 20 questions to earn a verifiable Time-Series Forecasting with Machine Learning certificate (SOA-TSFM). Every question is based on published standards and research — including Forecasting: Principles and Practice (Hyndman and Athanasopoulos), Common pitfalls and recommended practices (scikit-learn documentation), Time series / date functionality (pandas documentation) — and each answer is explained with its source.

Science of Apps offers a free Time-Series Forecasting with Machine Learning Certification, written for 2026 and 2027. It tests time-series foundations, statistical and ML models, evaluation and advanced and production with 20 original scenario questions in all, each naming its published source; you get an instant score and a verifiable certificate with a credential ID you can add to LinkedIn.

Read the four skill areas on this page, then take the 20-minute test online — no fee, course or sign-up. You see every answer explained when you finish. Score 70% or more and you get a Time-Series Forecasting with Machine Learning certificate straight away, on screen, as a PDF and by email, with a credential ID anyone can verify.

Yes. The questions are original scenarios written by Science of Apps, and each one is based on a public source you can read: Forecasting: Principles and Practice (Hyndman and Athanasopoulos); Common pitfalls and recommended practices (scikit-learn documentation); Time series / date functionality (pandas documentation); Ensembles: gradient boosting, random forests and more (scikit-learn); Cross-validation: evaluating estimator performance (scikit-learn); Metrics and scoring: quantifying the quality of predictions (scikit-learn). The source is shown under each question and linked in the answer review. Science of Apps doesn't copy questions from other tests.

Four skill areas with five questions each, drawn from a bank of 10 per area so a retake brings new questions: Time-Series Foundations (Components, Stationarity and autocorrelation and Baselines); Statistical and ML Models (Exponential smoothing and ARIMA, Feature-based ML and Deep learning); Evaluation (Time-series cross-validation, Error metrics and Leakage); Advanced and Production (Probabilistic forecasting, Hierarchies and drivers and Monitoring). Every question is an original scenario written to the 2026–2027 standard, with the published source named under it.

Data scientist or forecasting analyst, Demand planner or supply chain analyst, Machine learning engineer, Finance or energy analyst and Analytics or statistics student. It suits students at leading universities, career switchers and experienced professionals who want quick, verifiable proof of Time-Series Forecasting with Machine Learning knowledge in 2026 and 2027.

CS and AI students at MIT, Stanford, CMU, Berkeley, IITs, Tsinghua and ETH are hired on depth beyond tool use: model internals, systems and hardware. Passing earns a founder-signed Science of Apps certificate with a credential ID and QR code that recruiters can verify.

Random splits let the model train on future data and test on the past, leaking information. Time-series cross-validation uses rolling or expanding windows where training always precedes testing.

Mean absolute scaled error: the forecast's MAE divided by the in-sample MAE of a naive (or seasonal naive) forecast. Values below 1 mean the model beats the naive benchmark.

Yes. The test is online, in English and free everywhere — people take it from India, the US, the UK, Europe, Africa, Latin America and Asia-Pacific. Your certificate is dated with your own local date and can be verified from anywhere at scienceofapps.com/certification/verify/.

20 questions — 5 from each of 4 skill areas — with a 20-minute limit. You need 70% (14 of 20) to pass and 90% for distinction.

We rate it Intermediate: assumes hands-on experience in the role. It has 20 scenario questions — 5 from each of 4 skill areas — and a 20-minute limit. You need 70% (14 of 20) to pass and 90% for distinction.

Nothing. There is no fee to take a test, to receive the certificate, to have it emailed or to verify it, and no sign-up.

On our server, not in your browser. The answers never reach the page until you submit, so a certificate can only be earned by passing. Each certificate gets a unique credential ID stored by Science of Apps.

You see your score for each area and every question with the correct answer and why, with the published source behind it. You can retake after 30 minutes, and the retake draws questions you haven't seen yet.

No. Every certificate is dated, so it always shows when you passed. Retake a test any time for a newer date or a higher score.

Yes. After you pass, Add to LinkedIn opens the Licenses & certifications form with the certificate name, Science of Apps as the issuer, the issue date, your credential ID and the verification link filled in. You can also download it as a PDF or PNG, and it's emailed to you as a PDF.

Every certificate has a credential ID and a QR code. Scanning it, or entering the ID at scienceofapps.com/certification/verify/, shows the holder's name, the certification, the score and the issue date from our records.

No. They are Science of Apps' own assessments of applied skill. They aren't issued by a university or standards body, and they work best alongside a portfolio that shows the skills in real work.