Neuron-Synapse Level Problem Decomposition Method for Cooperative Neuro-Evolution of Feedforward Networks for Time Series Prediction

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series. Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

Download Full-text

Temperature time series prediction based on autoregressive integrated moving average model

Instrumentation Mesure Métrologie ◽

10.3166/i2m.17.443-453 ◽

2018 ◽

Vol 18 (3) ◽

pp. 443-453

Author(s):

Huanhuan ZHENG ◽

Yuxiu BAI ◽

Yaqiong ZHANG

Keyword(s):

Time Series ◽

Moving Average ◽

Time Series Prediction ◽

Temperature Time Series ◽

Average Model ◽

Autoregressive Integrated Moving Average ◽

Moving Average Model ◽

Temperature Time

Download Full-text

Making investment decisions in stock markets using a forecasting-Markowitz based decision-making approaches

Journal of Modelling in Management ◽

10.1108/jm2-12-2018-0217 ◽

2019 ◽

Vol 15 (2) ◽

pp. 647-659 ◽

Cited By ~ 1

Author(s):

Zahra Moeini Najafabadi ◽

Mehdi Bijari ◽

Mehdi Khashei

Keyword(s):

Decision Making ◽

Time Series ◽

Stock Markets ◽

Time Series Prediction ◽

Investment Decisions ◽

Rate Of Return ◽

Expected Returns ◽

Prediction Methods ◽

Content Type ◽

Markowitz Model

Purpose This study aims to make investment decisions in stock markets using forecasting-Markowitz based decision-making approaches. Design/methodology/approach The authors’ approach offers the use of time series prediction methods including autoregressive, autoregressive moving average and artificial neural network, rather than calculating the expected rate of return based on distribution. Findings The results show that using time series prediction methods has a significant effect on improving investment decisions and the performance of the investments. Originality/value In this study, in contrast to previous studies, the alteration in the Markowitz model started with the investment expected rate of return. For this purpose, instead of considering the distribution of returns and determining the expected returns, time series prediction methods were used to calculate the future return of each asset. Then, the results of different time series methods replaced the expected returns in the Markowitz model. Finally, the overall performance of the method, as well as the performance of each of the prediction methods used, was examined in relation to nine stock market indices.

Download Full-text