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Predicting lead times of purchase orders using gradient boosting machine
(2020)
A company can make more accurate predictions of its internal processes and sales lead
times when it has accurate predictions of the lead times of purchase orders. It results in more
efficient processes as well as improved ...
Investigating the impact of Brexit on the exchange rate volatility of the pound sterling with respect to the Euro and the US dollar
(2020)
This thesis investigates did Brexit affect on the exchange rate volatility of the British pound sterling towards the Euro (GBP/EUR) and towards the U.S. dollar (GBP/USD). The observed time frame is from January 2011 to ...
Detecting factors affecting contract terminations in the electricity distribution system
(2022)
Distribution system contract terminations are a factor of economic interest in the electricity market, as they affect the willingness of the distribution system operators (DSOs) to invest in the improvement of the distribution ...
Predicting imbalance power price
(2021)
The electricity market is a complex multi-layer system that is designed to balance power consumption and production at every single moment. This thesis focuses on the lowest level of the Finnish electricity market called ...
Comparison of machine learning algorithms in forest tree species classification from aerial images
(2022)
Remotely sensed data, such as satellite and aerial imagery, allow data to be obtained without visiting the area or physical contact with the area to be studied. Collecting remotely sensed data is faster and cheaper than ...
Machine learning techniques applied to energy behavior profiling
(2022)
The European Union has set a goal to reduce greenhouse gas emissions from the year 1990 by at least 55% by 2030. To achieve the goal, sustainable use of energy resources needs to be utilized. This study consists of conducting ...
Classifying customer companies in an enterprise resource planning system using machine learning methods
(2022)
Information systems such as smart phone applications collect large amounts of data about their users. The data is used mostly for the system’s primary task, but machine learning methods can be used to get additional value ...
Developing and comparing machine learning models for building heating energy consumption forecasting
(2022)
The goal of this thesis was to develop a forecasting model using statistical learning for a smart building company, Company X, to predict future heating energy consumption based on past weather and consumption data. Three ...
Material classification in the industry
(2023)
In the industry material classification and quality control are key challenges that require speed and accuracy. Automation of material classification using machine learning classifiers would provide a unified method that ...
Predicting late payment of sales invoices with statistical learning methods
(2023)
Predicting the time at which a company is bound to receive money from their outstanding sales invoices is a part of cash flow forecasting, which helps companies make better financial decisions. This study explores the ...