Comparison Analysis of Time Series Data Algorithm Complexity for Forecasting of Dengue Fever Occurrences
Main Article Content
Abstract
Times series complexity is a testing standard of a particular algorithm to achieve efficient time execution when it is implemented into programming language. Complexity of the algorithm is differentiated into two parts; those are time series complexity and space complexity. Time series complexity is determined from the numbers of computation steps needed to run algorithm as a function of several data n (input size). However, space series complexity is measured from the memory used by data structure found in the algorithm as the function of several data n. The objective of the study is to test the comparison of three forecasting algorithms among time series complexities by using operational empirical analysis with running time application when the algorithm is used in programming language or a particular application. The algorithms tested in this study were Linear Regression, SMO Regression, and Multilayer Perceptron by using Weka Application. The analysis result and the test result all three algorithms showed that running time was significantly influenced by algorithm’s complexity and additional data set numbers that became the input. Furthermore, running time analysis from those three algorithms showed stable rate respectively from SMO Regression, Linear Regression, and Multilayer Perceptron. However, for Big-O algorithm analysis Linear Regression and SMO Regression had almost similar time complexity, but they had far different from Multilayer Perceptron that tended to be more complex.
Keywords: time complexity, time series data, linear regression, SMO Regression, multilayer perceptron, Big-O
Downloads
Article Details
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.