Hybridization of nonlinear and linear models for time series forecasting
Nowadays, time series forecasting is very challenging due to the uncertainties of real world events that are influenced by many indefinite factors and rapid changes. This scenario requires forecasting methods that work efficiently with incomplete and multivariate data. Otherwise, the solutions will...
محفوظ في:
المؤلف الرئيسي: | Salleh@Sallehuddin, Roselina |
---|---|
التنسيق: | أطروحة |
اللغة: | English |
منشور في: |
2010
|
الموضوعات: | |
الوصول للمادة أونلاين: | http://eprints.utm.my/id/eprint/18768/1/HamisanSalimMFP2010.pdf |
الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
مواد مشابهة
-
The comparative forecast performance of univariate and multivariate model: an application to time series forecasting
بواسطة: Sallehuddin, Roselina, وآخرون
منشور في: (2009) -
The enhanced group method of data handling models for time series forecasting
بواسطة: Samsudin, Ruhaidah
منشور في: (2012) -
Time series modeling and designing of artifical neural network (ANN) for revenue forecasting
بواسطة: Mohd. Yusof, Norfadzlia
منشور في: (2005) -
A hybrid approach based on arima and artificial neural networks for crime series forecasting
بواسطة: Mohd. Zaki, Mohd. Suhaimi
منشور في: (2014) -
Combining group method of data handling models using artificial bee colony algorithm for time series forecasting
بواسطة: Yahya, Nurhaziyatul Adawiyah
منشور في: (2019)