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Decision Tree Classification and Forecasting of Pricing Time Series Data
KTH, School of Electrical Engineering (EES), Automatic Control.
2014 (English)Student paper other, 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Many companies today, in different fields of operations and sizes, have access

to a vast amount of data which was not available only a couple of years ago.

This situation gives rise to questions regarding how to organize and use the

data in the best way possible.

In this thesis a large database of pricing data for products within various market

segments is analysed. The pricing data is from both external and internal

sources and is therefore confidential. Because of the confidentiality, the labels

from the database are in this thesis substituted with generic ones and the

company is not referred to by name, but the analysis is carried out on the

real data set. The data is from the beginning unstructured and difficult to

overlook. Therefore, it is first classified. This is performed by feeding some

manual training data into an algorithm which builds a decision tree. The

decision tree is used to divide the rest of the products in the database into

classes. Then, for each class, a multivariate time series model is built and each

product’s future price within the class can be predicted. In order to interact

with the classification and price prediction, a front end is also developed.

The results show that the classification algorithm both is fast enough to operate

in real time and performs well. The time series analysis shows that it is possible

to use the information within each class to do predictions, and a simple vector

autoregressive model used to perform it shows good predictive results.

Place, publisher, year, edition, pages
2014. , 79 p.
National Category
Control Engineering
URN: urn:nbn:se:kth:diva-151017OAI: diva2:746332
Available from: 2014-09-12 Created: 2014-09-12 Last updated: 2014-09-12Bibliographically approved

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