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27 results for “Financial Time Series”
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Research ArticleOpen accessPhrase in the titleComparative Analysis of Deep Learning Models for Multi-Step Prediction of Financial Time SeriesSaugat Aryal, Dheynoshan Nadarajah, Prabath Lakmal Rupasinghe et al.Financial time series prediction has been a key topic of interest among researchers considering the complexity of the domain and also due to its significant impact on a wide range of applications. In contrast to one-step ahead prediction, multi-step forecasting is more desirable in the industry but the task is more challenging. In recent days, advancement in deep learning has … …Journal of Computer Science, Vol. 16, Issue 10 · 27 Oct 2020 · 6,967 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleIndex Financial Time Series Based on Zigzag-Perceptually Important PointsAli Selamat, Chawalsak Phetchanchai, Tanzila Saba, Amjad RehmanProblem statement: Financial time series were usually large in size, unstructured and of high dimensionality. Since, the illustration of financial time series shape was typically characterized by a few number of important points. These important points moved in zigzag directions which could form technical patterns. However, these important points exhibited in different resolutions and difficult to determine. Approach: In this … …Journal of Computer Science, Vol. 6, Issue 12 · 2 Nov 2010 · 9,464 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleFinancial Development and Economic Growth in Nepal: New intuitions from a Time Series Causality MethodUttam Paudel, Satish Chandra Bhatta, Umesh KhatriGeneralized empirical evidences about impact and direction of causality between financial development and economic growth ignoring the differences of structure and other factors seem less effective to understand the contributions of financial as well as real sector development to economic growth by country specific factors in Nepal. This paper has established the short run or long run relationship and the … …Journal of Social Sciences, Vol. 14, Issue 1 · 28 Mar 2018 · 8,474 accessesView PDFCite
Research ArticleOpen accessIn the keywordsA Hybridization of Enhanced Artificial Bee Colony-Least Squares Support Vector Machines for Price ForecastingZuriani Mustaffa, Yuhanis YusofProblem statement: As the performance of Least Squares Support Vector Machines (LSSVM) is highly rely on its value of regularization parameter, γ and kernel parameter, σ2, man-made approach is clearly not an appropriate solution since it may lead to blindness in certain extent. In addition, this technique is time consuming and unsystematic, which consequently affect the generalization performance of LSSVM. … …Journal of Computer Science, Vol. 8, Issue 10 · 28 Aug 2012 · 6,796 accessesView PDFCite
Research ArticleOpen accessIn the keywordsVolatility in Malaysian Stock Market: An Empirical Study Using Fractionally Integrated ApproachChin Wen CheongThis study explores the fractionally integrated (FI) time series analysis in Malaysian stock market. Four proxies of latent volatility, namely the absolute return, squared return and range-based (Parkinson and Garman and Klass) volatilities are selected for the empirical studies. In addition, the well-known FI autoregressive conditional variance (ARCH) type model is also taken into account for comparison purposes. Our empirical … …American Journal of Applied Sciences, Vol. 5, Issue 6 · 30 Jun 2008 · 6,706 accessesView PDFCite
Research ArticleOpen accessIn the keywordsThe Gaussianity Evaluations of Malaysian Stock Return VolatilityChin Wen CheongWe study the distribution of standardized returns by using various frequencies data. The empirical standardized returns are obtained by using the unobserved and observable daily volatility. Our empirical results evidence the realized-standardized returns follow nearest to a Gaussian distribution. On the other hand, the standardized returns using daily closing and range-based data are able to reduce but not fully eliminate … …American Journal of Applied Sciences, Vol. 5, Issue 2 · 28 Feb 2008 · 7,078 accessesView PDFCite
Research ArticleOpen accessIn the abstractDeep Learning Models for Predicting Stock Closing Prices in the Saudi Stock MarketAreej Alhumaidi, Hoda A. AbdelhafezFinancial time series data are inherently volatile and nonlinear, presenting considerable challenges in accurately forecasting stock market trends. This study aims to predict the closing prices of companies listed on the Saudi stock market by analyzing historical data spanning from 2013 to 2023. To this end, four deep learning models were employed-Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent … …Journal of Computer Science, Vol. 22, Issue 4 · 22 Apr 2026 · 1,355 accessesView PDFCite
Research ArticleOpen accessIn the abstractStock Closing Price Forecasting Using LSTM, Sentiment Analysis, Kalman FilterAssel Abdildayeva, Galymzhan Nurtugan, Guldana Taganova, Ardak AkhmetovaThe article explores the problem of predicting the closing prices of stocks using an innovative approach that combines methods of machine learning, sentiment analysis and noise filtering. The focus is on the development and testing of a comprehensive model that integrates recurrent neural networks (LSTM) for time series analysis, the GPT-3 language model for processing and analyzing textual data from … …Journal of Computer Science, Vol. 20, Issue 11 · 4 Sep 2024 · 6,262 accessesView PDFCite
Research ArticleOpen accessIn the abstractSales Forecasting Models: Comparison between ARIMA, LSTM and ProphetDmitry BrykinSales forecasting is crucial for business planning and resource allocation. Data-driven approaches have become popular in this field. This study compares the performance of three forecasting models: Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM) and Prophet within the context of specific sales categories derived from acquiring data provided by a bank. This study uses a time series dataset … …Journal of Computer Science, Vol. 20, Issue 10 · 1 Aug 2024 · 7,981 accessesView PDFCite
Research ArticleOpen accessIn the abstractForecasting of Multistep Multivariate Financial Data Through GSO Algorithm Infused Vanilla LSTM ModelNikhitha Pai, Ilango Velchamy, Nithya B RameshPredicting time series in the financial domain requires using models that can do sequence processing tasks. One such model is the LSTM model which belongs to the family of deep learning networks. The performance measure of LSTM in a regression task is to bring down the error in the predicted values. In this study, a novel fused LSTM model integrating … …Journal of Computer Science, Vol. 19, Issue 7 · 10 Jul 2023 · 6,074 accessesView PDFCite