Time Series Forecasting with Machine Learning Methods Course

Master time-series forecasting with statistical and machine-learning methods, valid back-testing, uncertainty and operational monitoring.

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Training Locations

This Time Series Forecasting with Machine Learning Methods Course is available in multiple cities. Please select your preferred location from the list below

Durrës

Albania

Tirana

Albania

Andorra la Vella

Andorra

Escaldes-Engordany

Andorra

Innsbruck

Austria

Salzburg

Austria

Vienna

Austria

Gomel

Belarus

Minsk

Belarus

Antwerp

Belgium

Brussels

Belgium

Banja Luka

Bosnia and Herzegovina

Sarajevo

Bosnia and Herzegovina

Plovdiv

Bulgaria

Sofia

Bulgaria

Dubrovnik

Croatia

Split

Croatia

Zagreb

Croatia

Limassol

Cyprus

Nicosia

Cyprus

Brno

Czech Republic

Prague

Czech Republic

Aarhus

Denmark

Copenhagen

Denmark

Tallinn

Estonia

Tartu

Estonia

Helsinki

Finland

Tampere

Finland

Lyon

France

Marseille

France

Nice

France

Paris

France

Berlin

Germany

Frankfurt

Germany

Hamburg

Germany

Munich

Germany

Athens

Greece

Thessaloniki

Greece

Budapest

Hungary

Debrecen

Hungary

Akureyri

Iceland

Reykjavík

Iceland

Cork

Ireland

Dublin

Ireland

Florence

Italy

Milan

Italy

Naples

Italy

Rome

Italy

Pristina

Kosovo

Prizren

Kosovo

Liepāja

Latvia

Riga

Latvia

Schaan

Liechtenstein

Vaduz

Liechtenstein

Kaunas

Lithuania

Vilnius

Lithuania

Esch-sur-Alzette

Luxembourg

Luxembourg City

Luxembourg

St. Julian's

Malta

Valletta

Malta

Bălți

Moldova

Chișinău

Moldova

La Condamine

Monaco

Monte Carlo

Monaco

Budva

Montenegro

Podgorica

Montenegro

Amsterdam

Netherlands

Rotterdam

Netherlands

The Hague

Netherlands

Ohrid

North Macedonia

Skopje

North Macedonia

Bergen

Norway

Oslo

Norway

Gdańsk

Poland

Kraków

Poland

Warsaw

Poland

Faro

Portugal

Lisbon

Portugal

Porto

Portugal

Bucharest

Romania

Cluj-Napoca

Romania

City of San Marino

San Marino

Serravalle

San Marino

Belgrade

Serbia

Novi Sad

Serbia

Singapore

Bratislava

Slovakia

Košice

Slovakia

Bled

Slovenia

Ljubljana

Slovenia

Barcelona

Spain

Madrid

Spain

Valencia

Spain

Gothenburg

Sweden

Stockholm

Sweden

Bern

Switzerland

Geneva

Switzerland

Zurich

Switzerland

Kyiv

Ukraine

Lviv

Ukraine

Odesa

Ukraine

Dubai

United Arab Emirates

Birmingham

United Kingdom

Edinburgh

United Kingdom

London

United Kingdom

Manchester

United Kingdom

Rome (Vatican-adjacent)

Vatican City

Training Outlines

Introduction

Time-series forecasting supports decisions in demand, finance, workforce, maintenance, capacity and supply planning. Unlike ordinary predictive modelling, time-ordered data contains trend, seasonality, autocorrelation and structural change. Invalid data splitting or future information can create highly optimistic results that disappear when the model is used operationally.

This five-day course develops practical competence in forecasting with statistical and machine-learning methods. Participants will design valid back-tests, construct temporal features, compare classical and modern models, quantify uncertainty and connect forecast performance to planning decisions. Applied exercises use realistic operational series with changing conditions and multiple forecast horizons.

Objectives

By the end of this course, participants will be able to:

Analyse trend, seasonality and temporal dependence.
Design valid rolling and out-of-time evaluation.
Establish meaningful naïve and statistical baselines.
Engineer lag, rolling and calendar features.
Apply machine-learning models to temporal data.
Compare point, interval and probabilistic forecasts.
Select metrics aligned with planning consequences.
Design a monitored forecasting workflow.

Course Content

Day 1: Time-Series Structure and Forecast Design

Time index, frequency and granularity
Trend, seasonality and cycles
Autocorrelation and temporal dependence
Forecast horizons and decision timing
Rolling validation and temporal leakage
Exploratory forecasting laboratory

Day 2: Statistical Forecasting Foundations

Naïve and seasonal baselines
Moving averages and exponential smoothing
Stationarity and transformation
ARIMA and seasonal ARIMA
Exogenous variables and intervention effects
Residual-diagnostic laboratory

Day 3: Machine-Learning Forecasting

Lag and rolling-window features
Calendar and event features
Linear and regularised models
Tree and gradient-boosting methods
Recursive and direct forecasting
Feature-based forecasting laboratory

Day 4: Advanced Forecast Design

Multi-step and multi-output forecasting
Quantile and probabilistic forecasts
Sequence and deep-learning models
Model ensembles and combinations
Time-aware tuning and back-testing
Comparative forecast experiment

Day 5: Evaluation and Operational Forecasting

MAE, RMSE and scaled errors
Bias and interval coverage
Segment and hierarchy performance
Drift and structural-break monitoring
Retraining and forecast governance
Capstone operational forecasting solution

Training Schedule

Below is the table of cities along with the respective dates for the upcoming training sessions of Time Series Forecasting with Machine Learning Methods Course. Please review the schedule to find the most convenient option for you. You can also use the below search bar to type the city name and filter the results.