Online or onsite, instructor-led live Forecasting training courses demonstrate through interactive hands-on practice how to use statistical methods, machine learning techniques, and time series analysis to analyze historical data, identify patterns, and make accurate predictions for future trends.
Forecasting training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Bonn onsite live Forecasting trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
Our training facilities are located at Mozartstraße 4-10 in Bonn. Our spacious training rooms are located southwest of the city centre and offer optimal training conditions for your needs.
Arrival
The NobleProg training facilities are conveniently located near the Bonn main station. In the west you reach the motorway A565.
Parking
You will find numerous parking spaces around our training rooms.
Local Infrastructure
In downtown Bonn you will find numerous hotels and restaurants..
This course has been created for analysts, forecasters wanting to introduce or improve forecasting which can be related to sale forecasting, economic forecasting, technology forecasting, supply chain management and demand or supply forecasting.
Description
This course guides delegates through series of methodologies, frameworks and algorithms which are useful when choosing how to predict the future based on historical data.
It uses standard tools like Microsoft Excel or some Open Source programs (notably R project).
The principles covered in this course can be implemented by any software (e.g. SAS, SPSS, Statistica, MINITAB ...)
This instructor-led, live training in Bonn (online or onsite) is aimed at intermediate-level data analysts and business professionals who wish to perform time series forecasting and automate data analysis workflows using R.
By the end of this training, participants will be able to:
Understand the fundamentals of forecasting techniques in R.
Apply exponential smoothing and ARIMA models for time series analysis.
Utilize the ‘forecast’ package to generate accurate forecasting models.
Automate forecasting workflows for business and research applications.
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Testimonials (2)
1.The presentation style of Mr. Andrew, engaging with the style of audience.
MUHAMMAD ZAID SALMAN - GAS MALAYSIA DISTRIBUTION SDN BHD
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