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. Onsite live Forecasting training can be carried out locally on customer premises in Moldova or in NobleProg corporate training centers in Moldova.
Situated right in the city center, you can see and feel history around this hotel. It has ten conference rooms equipped with high speed internet and the latest technologies needed for a successful conference, training or meeting. It can accommodate from four to two hundert forty persons and the hotel team is always open to help. The underground spa offers a place where you can relax after a full day..
Audience
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 Moldova (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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