Feature Selection in KNIME

Nisbet, Bob

doi:10.1016/b978-0-12-416632-5.00027-x

Cited by 4 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(Box [25], 1976) Occam's razor (a principle also known as parsimony) is often used to avoid the danger of overfitting the training data, that is, to choose a model that perfectly fits the time series data but is very complex and hence often does not generalize well. Time series analysis sometimes follows this principle in that many ARIMA or SARIMA models perform increasingly worse as the number of historical values used to forecast y t+1 increases [26].…”

Section: Arima Time Series Modelingmentioning

confidence: 99%

A Literature Review on Some Trends in Artificial Neural Networks for Modeling and Simulation with Time Series

Muñoz-Zavala,

Macías-Díaz,

Alba-Cuéllar

et al. 2024

Algorithms

View full text Add to dashboard Cite

This paper reviews the application of artificial neural network (ANN) models to time series prediction tasks. We begin by briefly introducing some basic concepts and terms related to time series analysis, and by outlining some of the most popular ANN architectures considered in the literature for time series forecasting purposes: feedforward neural networks, radial basis function networks, recurrent neural networks, and self-organizing maps. We analyze the strengths and weaknesses of these architectures in the context of time series modeling. We then summarize some recent time series ANN modeling applications found in the literature, focusing mainly on the previously outlined architectures. In our opinion, these summarized techniques constitute a representative sample of the research and development efforts made in this field. We aim to provide the general reader with a good perspective on how ANNs have been employed for time series modeling and forecasting tasks. Finally, we comment on possible new research directions in this area.

show abstract

Section: Arima Time Series Modelingmentioning

confidence: 99%