Antti Sorjamaa: Methodologies for Time Series Prediction

Antti Sorjamaa from the Adaptive Informatics Research Centre at Aalto University School of Science and Technology is depending his PhD Thesis Methodologies for Time Series Prediction and Missing Value Imputation. He is stressing the importance of direct prediction in the case of predicting multiple future values instead of a recursive approach in which accumulation of errors is a clear problem. He also motivates the importance of considering missing values as in many applications there can be a large number of missing measurements. As an example, he uses the environmental state of Tanganjika Lake. The opponent in the defence is Prof. Guilherme de Alencar Barreto from Brazil and the custos is Prof. Olli Simula. The work has been conducted under the supervision of Dr. Amaury Lendasse who is responsible for time series analysis research in the research center.