Forecasting Implementation of Hybrid Time Series and Artificial Neural Network Models
DL Polestico, AL Bangcale, LC Velasco - Procedia Computer Science, 2024 - Elsevier
This study implemented AR, SARIMA, and SETAR models and their hybrid with ANN using
the Canadian lynx data. Implementing a SETAR-ANN has been shown to be successful in …
the Canadian lynx data. Implementing a SETAR-ANN has been shown to be successful in …
A novel hybridization of artificial neural networks and ARIMA models for time series forecasting
Improving forecasting especially time series forecasting accuracy is an important yet often
difficult task facing decision makers in many areas. Both theoretical and empirical findings …
difficult task facing decision makers in many areas. Both theoretical and empirical findings …
A new hybrid methodology for nonlinear time series forecasting
Artificial neural networks (ANNs) are flexible computing frameworks and universal
approximators that can be applied to a wide range of forecasting problems with a high …
approximators that can be applied to a wide range of forecasting problems with a high …
[HTML][HTML] Forecasting nonlinear time series with a hybrid methodology
In recent years, artificial neural networks (ANNs) have been used for forecasting in time
series in the literature. Although it is possible to model both linear and nonlinear structures …
series in the literature. Although it is possible to model both linear and nonlinear structures …
Time series forecasting using a hybrid ARIMA and neural network model
GP Zhang - Neurocomputing, 2003 - Elsevier
Autoregressive integrated moving average (ARIMA) is one of the popular linear models in
time series forecasting during the past three decades. Recent research activities in …
time series forecasting during the past three decades. Recent research activities in …
A novel hybridization of ARIMA, ANN, and K-means for time series forecasting
W Pannakkong, VH Pham, VN Huynh - Research Anthology on …, 2022 - igi-global.com
This article aims to propose a novel hybrid forecasting model involving autoregressive
integrated moving average (ARIMA), artificial neural networks (ANNs) and k-means …
integrated moving average (ARIMA), artificial neural networks (ANNs) and k-means …
Time series forecasting. A comparative study between an evolving artificial neural networks system and statistical methods
JP Donate, GG Sanchez… - International Journal on …, 2012 - World Scientific
Accurate time series forecasting are important for displaying the manner in which the past
continues to affect the future and for planning our day to-day activities. In recent years, a …
continues to affect the future and for planning our day to-day activities. In recent years, a …
Which methodology is better for combining linear and nonlinear models for time series forecasting?
Both theoretical and empirical findings have suggested that combining different models can
be an effective way to improve the predictive performance of each individual model. It is …
be an effective way to improve the predictive performance of each individual model. It is …
A study on time series forecasting using hybridization of time series models and neural networks
Introduction: Auto-Regressive Integrated Moving Average (ARIMA) and Artificial Neural
Networks (ANN) are leading linear and non-linear models in Machine learning respectively …
Networks (ANN) are leading linear and non-linear models in Machine learning respectively …
A novel hybridization of arima, ann, and k-means for time series forecasting
W Pannakkong, VH Pham, VN Huynh - International Journal of …, 2017 - igi-global.com
This article aims to propose a novel hybrid forecasting model involving autoregressive
integrated moving average (ARIMA), artificial neural networks (ANNs) and k-means …
integrated moving average (ARIMA), artificial neural networks (ANNs) and k-means …