Methodologies for Time Series Prediction and Missing Value Imputation

The amount of collected data is increasing all the time in the world. Even though computers are getting faster and faster, large databases still need better and more accurate methodologies for them to be useful in practice. Some techniques are not feasible to be applied to very large databases or are not able to provide the necessary accuracy. This book focuses on two aspects encountered with databases, time series prediction and missing value imputation. Accurate prediction of future values is heavily dependent not only on a good model, which is well trained and validated, but also preprocessing, input variable selection or projection and output approximation strategy selection. On the other hand, missing values can be a nuisance, but can also be a prohibiting factor in the use of certain methodologies and degrade the performance of others. Hence, missing value imputation is a very necessary part of the preprocessing of a database. Furthermore, even though the accuracy is always the main requisite for a good methodology, this book touches the aspect of computational time, which has to be considered with the accuracy in order to reach good results.

Autorentext

Antti Sorjamaa received his master degree from Helsinki University of Technology in 2005. After the master degree, he continued the work towards doctoral degree and graduated in 2010. The covered topics of the research and this book are a combination of Missing Value Problems in Spatio-Temporal Databases and Long-Term Time Series Prediction.