In line with technology advancements in processing power and storing capabilities through cloud services, higher demands are set on companies’ data sets. Business executives are now expecting analyses of real time data or massive data sets, where traditional Business Intelligence struggle to deliver. The interest of using machine learning to predict trends and patterns which the human eye can’t see is thus higher than ever. Time series data sets are data sets characterised by a time stamp and a value; for example, a sensor data set. The company with which I’ve been in touch collects data from sensors in a control room. In order to predict patterns and in the future using these in combination with other data, the company wants to apply machine learning on their data set. To do this effectively, the right machine learning model needs to be selected. This thesis therefore has the purpose of finding out which machine learning model, or models, from the selected platform – Azure Machine Learning Studio – works best on a time series data set with sensor data. The models are then tested through a machine learning pilot on the company’s data Throughout the thesis, multiple machine learning models from the selected platform are evaluated. For the data set in hand, the conclusion is that a supervised regression model by the type of a Decision Forest Regression model gives the best results and has the best chance to adapt to a data set growing in size. Another conclusion is that more training data is needed to give the model an even better result, especially since it’s taking date and week day into account. Adjustments of the parameters for each model might also affect the result, opening up for further improvements.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:ltu-71223 |
| Date | January 2018 |
| Creators | Johansson, Richard |
| Publisher | Luleå tekniska universitet, Institutionen för system- och rymdteknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | Swedish |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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