scholarly journals COMPUTATIONAL INTELLIGENCE METHODS FOR FINANCIAL TIME SERIES MODELING

2006 ◽  
Vol 16 (07) ◽  
pp. 2053-2062 ◽  
Author(s):  
N. G. PAVLIDIS ◽  
D. K. TASOULIS ◽  
V. P. PLAGIANAKOS ◽  
M. N. VRAHATIS
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Exchange Rate ◽  
Ad Hoc ◽  
Clustering Algorithms ◽  
Real Life ◽  
Feedforward Neural Networks ◽  
Unsupervised Clustering ◽  
Space Partitioning ◽  
Input Space

In this paper, the combination of unsupervised clustering algorithms with feedforward neural networks in exchange rate time series forecasting is studied. Unsupervised clustering algorithms have the desirable property of deciding on the number of partitions required to accurately segment the input space during the clustering process, thus relieving the user from making this ad hoc choice. Combining this input space partitioning methodology with feedforward neural networks acting as local predictors for each identified cluster helps alleviate the problem of nonstationarity frequently encountered in real-life applications. An improvement in the one-step-ahead forecasting accuracy was achieved compared to a global feedforward neural network model for the time series of the exchange rate of the German Mark to the US Dollar.

scholarly journals Learning in Feedforward Neural Networks Accelerated by Transfer Entropy

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 102 ◽  
Author(s):  
Adrian Moldovan ◽  
Angel Caţaron ◽  
Răzvan Andonie
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Information Transfer ◽  
Feedforward Neural Networks ◽  
Theoretical Method ◽  
Transfer Entropy ◽  
Training Algorithms ◽  
Training Algorithm ◽  
Neural Connections ◽  
Feedback Connections

Current neural networks architectures are many times harder to train because of the increasing size and complexity of the used datasets. Our objective is to design more efficient training algorithms utilizing causal relationships inferred from neural networks. The transfer entropy (TE) was initially introduced as an information transfer measure used to quantify the statistical coherence between events (time series). Later, it was related to causality, even if they are not the same. There are only few papers reporting applications of causality or TE in neural networks. Our contribution is an information-theoretical method for analyzing information transfer between the nodes of feedforward neural networks. The information transfer is measured by the TE of feedback neural connections. Intuitively, TE measures the relevance of a connection in the network and the feedback amplifies this connection. We introduce a backpropagation type training algorithm that uses TE feedback connections to improve its performance.

scholarly journals Estimation of the development of the Euro to Chinese Yuan exchange rate using artificial neural networks

2019 ◽  
Vol 61 ◽  
pp. 01030 ◽  
Author(s):  
Marek Vochozka ◽  
Jaromír Vrbka
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Time Series ◽  
Exchange Rate ◽  
Financial Institutions ◽  
Current Status ◽  
Medium Term ◽  
Future Developments ◽  
Long Run ◽  
Chinese Yuan

The exchange rate is one of the most monitored economic variables, from the position of individual citizens or economists, financial institutions or entrepreneurs. In the long run, it is a reflection of the condition of the economy, and in the short and medium term it has a significant impact on the economy. The time series of currency development maps past developments, current status, and is also able to predict future developments. This article analyzes the time series of the development of EUR to Yuan exchange rate using artificial intelligence. It aims to evaluate this development and to indicate the prediction of the future development of EUR to Yuan.

A Hybrid Validity Index to Determine K Parameter Value of k-Means Algorithm for Time Series Clustering

2021 ◽  
pp. 1-22
Author(s):  
Fatma Ozge Ozkok ◽  
Mete Celik
Keyword(s):  
Time Series ◽  
Clustering Algorithms ◽  
Real Life ◽  
Internal Validity ◽  
The Other ◽  
Sequential Data ◽  
Data Mining Technique ◽  
Validity Index ◽  
Number Of Clusters ◽  
Benchmark Datasets

Time series is a set of sequential data point in time order. The sizes and dimensions of the time series datasets are increasing day by day. Clustering is an unsupervised data mining technique that groups objects based on their similarities. It is used to analyze various datasets, such as finance, climate, and bioinformatics datasets. [Formula: see text]-means is one of the most used clustering algorithms. However, it is challenging to determine the value of [Formula: see text] parameter, which is the number of clusters. One of the most used methods to determine the number of clusters (such as [Formula: see text]) is cluster validity indexes. Several internal and external validity indexes are used to find suitable cluster numbers based on characteristics of datasets. In this study, we propose a hybrid validity index to determine the value of [Formula: see text] parameter of [Formula: see text]-means algorithm. The proposed hybrid validity index comprises four internal validity indexes, such as Dunn, Silhouette, C index, and Davies–Bouldin indexes. The proposed method was applied to nine real-life finance and benchmarks time series datasets. The financial dataset was obtained from Yahoo Finance, consisting of daily closing data of stocks. The other eight benchmark datasets were obtained from UCR time series classification archive. Experimental results showed that the proposed hybrid validity index is promising for finding the suitable number of clusters with respect to the other indexes for clustering time-series datasets.

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