Editor/Reviewer loginFrequency : 12 issues per year
Subject : Computer Applications and Technology
ISSN : 2319–8656 (Online)
IJCATR Volume 5 Issue 12
Time Series Forecasting Using Novel Feature Extraction Algorithm and Multilayer Neural Network
Raheleh Rezazadeh, Hooman Kashanian
10.7753/IJCATR0512.1004
keywords : time series data, neural network, forecasting
Time series forecasting is important because it can often provide the foundation for decision making in a large variety of fields. A tree-ensemble method, referred to as time series forest (TSF), is proposed for time series classification. The approach is based on the concept of data series envelopes and essential attributes generated by a multilayer neural network... These claims are further investigated by applying statistical tests. With the results presented in this article and results from related investigations that are considered as well, we want to support practitioners or scholars in answering the following question: Which measure should be looked at first if accuracy is the most important criterion, if an application is time-critical, or if a compromise is needed? In this paper demonstrated feature extraction by novel method can improvement in time series data forecasting process.
@artical{r5122016ijcatr05121004,
Title = "Time Series Forecasting Using Novel Feature Extraction Algorithm and Multilayer Neural Network",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "5",
Issue ="12",
Pages ="748 - 759",
Year = "2016",
Authors ="Raheleh Rezazadeh, Hooman Kashanian"}
The goal of a time series forecast is to identify factors that can be predicted
Two trained networks with identical beginning parameters can produce drastically different forecasts
By inspecting the data series and through trial-and-error, thirty network inputs were selected
Novel method can improvement in time series data forecasting process