Enriching time series datasets using nonparametric kernel regression to improve forecasting accuracy

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Improving the accuracy of prediction on future values based on the past and current observations has been pursued by enhancing the prediction's methods, combining those methods or performing data pre-processing. In this paper, another approach is taken, namely by increasing the number of input in the dataset. This approach would be useful especially for a shorter time series data. By filling the in-between values in the time series, the number of training set can be increased, thus increasing the generalization capability of the predictor. The algorithm used to make prediction is Neural Network as it is widely used in literature for time series tasks. For comparison, Support Vector Regression is also employed. The dataset used in the experiment is the frequency of USPTO's patents and PubMed's scientific publications on the field of health, namely on Apnea, Arrhythmia, and Sleep Stages. Another time series data designated for NN3 Competition in the field of transportation is also used for benchmarking. The experimental result shows that the prediction performance can be significantly increased by filling in-between data in the time series. Furthermore, the use of detrend and deseasonalization which separates the data into trend, seasonal and stationary time series also improve the prediction performance both on original and filled dataset. The optimal number of increase on the dataset in this experiment is about five times of the length of original dataset.

Original languageEnglish
Title of host publicationICACSIS 2011 - 2011 International Conference on Advanced Computer Science and Information Systems, Proceedings
Pages227-232
Number of pages6
Publication statusPublished - 1 Dec 2011
Event2011 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2011 - Jakarta, Indonesia
Duration: 17 Dec 201118 Dec 2011

Publication series

NameICACSIS 2011 - 2011 International Conference on Advanced Computer Science and Information Systems, Proceedings

Conference

Conference2011 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2011
Country/TerritoryIndonesia
CityJakarta
Period17/12/1118/12/11

Fingerprint

Dive into the research topics of 'Enriching time series datasets using nonparametric kernel regression to improve forecasting accuracy'. Together they form a unique fingerprint.

Cite this