Add to ORKGAbstract: In this paper, the concept of a long memory system for forecasting is developed. Pattern modelling and recognition systems are introduced as local approximation tools for forecasting. Such systems are used for matching the current state of the time-series with past states to make a forecast. In the past, this system has been successfully used for forecasting the Santa Fe competition data. In this paper, we forecast the financial indices of six different countries, and compare the results with neural networks on five different error measures. The results show that pattern recognition-based approaches in time-series forecasting are highly accurate, and that these are able to match the performance of advanced methods such as neural n...
Financial markets are highly complex and volatile; thus, learning about such markets for the sake of...
The analysis of financial time series is of primary importance in the economic world. This paper deal...
Numeric data obtained in time sequence is represented by a time series. The main objective of predic...
Abstract:- The increased popularity of financial time series forecasting in recent times lies to its...
The problem of forecasting a time series with a neural network is well-defined when considering a si...
Time Series Forecasting is vital for wide range of domains such as financial market forecasting, ear...
The problem of forecasting a time series with a neural network is well-defined when considering a si...
The problem of forecasting a time series with a neural network is well-defined when considering a si...
The problem of forecasting a time series with a neural network is well-defined when considering a si...
The problem of forecasting a time series with a neural network is well-defined when considering a si...
The selective pattern matching method for forecasting the increment signs of financial time series i...
Financial market forecasting is a challenging and complex task due to the sensitivity of the market ...
The increasing availability of large amounts of historical data and the need of performing accurate ...
A functional method for time series forecasting is presented. Based on the splitting of the past dyn...
Financial and economic time series forecasting has never been an easy task due to its sensibility to...
Financial markets are highly complex and volatile; thus, learning about such markets for the sake of...
The analysis of financial time series is of primary importance in the economic world. This paper deal...
Numeric data obtained in time sequence is represented by a time series. The main objective of predic...
Abstract:- The increased popularity of financial time series forecasting in recent times lies to its...
The problem of forecasting a time series with a neural network is well-defined when considering a si...
Time Series Forecasting is vital for wide range of domains such as financial market forecasting, ear...
The problem of forecasting a time series with a neural network is well-defined when considering a si...
The problem of forecasting a time series with a neural network is well-defined when considering a si...
The problem of forecasting a time series with a neural network is well-defined when considering a si...
The problem of forecasting a time series with a neural network is well-defined when considering a si...
The selective pattern matching method for forecasting the increment signs of financial time series i...
Financial market forecasting is a challenging and complex task due to the sensitivity of the market ...
The increasing availability of large amounts of historical data and the need of performing accurate ...
A functional method for time series forecasting is presented. Based on the splitting of the past dyn...
Financial and economic time series forecasting has never been an easy task due to its sensibility to...
Financial markets are highly complex and volatile; thus, learning about such markets for the sake of...
The analysis of financial time series is of primary importance in the economic world. This paper deal...
Numeric data obtained in time sequence is represented by a time series. The main objective of predic...