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Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Dmitriy Kurganskiy
Time Series Prediction Based on Machine Learning Techniques for Gas and Oil Industry
Data Science
(Master’s programme)
2018
This work is part of a research project for Schlumberger and is devoted to the task of determining the working state of holes formed by hydraulic fracturing in oil mines.

Since each of the mines has more than one hole, the problem of multi-space (multi label) classification was formulated, in which the indicative space is represented by time series of several sensors. The data obtained from the Schlumberger mathematical generator are used as the initial data.

The paper considers and tests methods of extracting significant features from time series, methods of data normalization and space dimension reduction. Also, several basic algorithms, an ensemble of basic algorithms were tested and a cascade of two algorithms was proposed.

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