Results 61 to 70 of about 19,064 (239)

Comparison of LSTM, GRU, Bi-LSTM, and XGBoost for BRIS Stock Price Prediction with Lookback Period Variations

Journal of Applied Informatics and Computing
Stock price forecasting remains a challenging task due to the nonlinear and non-stationary characteristics of financial time series, particularly for Islamic banking stocks such as Bank Syariah Indonesia (BRIS), which exhibit highly dynamic price ...
Subekti Wahyu Aji, Wise Herowati
doaj   +1 more source

The genome sequence of the common crane, Grus grus (Linnaeus, 1758)

Wellcome Open Research
We present a genome assembly from a male specimen of Grus grus (common crane; Chordata; Aves; Gruiformes; Gruidae). The assembly contains two haplotypes with total lengths of 1,352.26 megabases and 1,291.08 megabases.
O’Brien, Michelle F., Lopez Colom, Rosa   +1 more
openaire   +2 more sources

A Novel Text‐Based Framework for Forecasting Carbon Prices

Journal of Forecasting, EarlyView.
ABSTRACT This study proposes a text‐based framework for predicting EU carbon prices. Using weekly data from 2020 to 2024, we construct a multivariate dataset combining financial indicators, commodity prices, Google Trends measures, and news‐based sentiment extracted using FinBERT.
Christian Oliver Ewald, Yaoyu Li
wiley   +1 more source

Deep Learning-Enhanced Comparative Analysis of ARIMA, Seasonal ARIMA, and Gated Recurrent Unit Models for Forecasting Car Sales

International Journal of Informatics, Information System and Computer Engineering
This study aims to assess and compare the performance of three forecasting models—Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Gated Recurrent Unit (GRU)—in predicting Toyota car sales ...
Fariz Zakaria, Ema Utami
doaj   +1 more source

Predicting monthly gold prices in indian rupees using ARIMA, LSTM, GRU, and Simple Linear Regression models [PDF]

Revista Română de Informatică și Automatică
For investors and financial analysts to make informed decisions, having precise forecasts of gold prices is crucial. This study examined the effectiveness of various time series models in predicting gold prices in Indian Rupeea variety of models, ranging
Hanan ALJOHANI, Sawsan ALSHAMRANI, Nahla ALJOJO, Araek TASHKANDI, Tariq ALSAHFI   +4 more
doaj   +1 more source

Beyond Prediction: Data, Baselines, Explanation, and Causation in Machine Learning for Food Insecurity

Food Safety and Health, EarlyView.
The gains from machine learning in nowcasting and forecasting food insecurity are still small and limited and cannot be observed in all countries. This review summarizes the public resources for data, corrects common misconceptions about model requirements, and establishes baseline requirements, explanations, causal inference, and equity in operational
Shabnam Mehboob, Azman Abdullah, Gunesh Kumar, Samreen Memon, Naila Afghan   +4 more
wiley   +1 more source

Multi‐command wearable magnetic human‐machine interface enabled by low‐noise sensing and time‐series deep learning

InfoScience, EarlyView.
This study integrates a wrist‐worn sensing platform based on a low‐noise planar Hall magnetoresistive sensor with time‐series deep learning to enable long‐range, single‐sensor, multi‐command gesture recognition with high accuracy. Further evaluations show reliable generalization across different days, previously unseen users, wearing‐position ...
Guannan Mu, Proloy Taran Das, Jan Schmidtpeter, Andreas Knüpfer, Rui Xu, Denys Makarov   +5 more
wiley   +1 more source

Machine Learning‐Based Estimation of Reference Evapotranspiration and Crop Coefficients for Wheat Under Diverse Climatic Conditions

Irrigation and Drainage, EarlyView.
ABSTRACT Accurate estimation of reference evapotranspiration (ET0) and crop coefficients (Kc) is critical for irrigation planning, particularly in data‐limited regions where agriculture dominates freshwater consumption. Although machine learning (ML) methods have been widely applied to ET0 and Kc estimation, most studies address these parameters ...
Ilker Angin, Hasan Er, Mete Yaganoglu, Muhammed Sungur Demir, Abdullah Muratoglu   +4 more
wiley   +1 more source

Time‐Frequency Market‐State Learning for Execution‐Aware Directional Prediction: Evidence From the CSI 300 Index

International Studies of Economics, EarlyView.
ABSTRACT Financial markets can be viewed as multiscale information systems in which short‐lived disturbances, recurrent temporal patterns, liquidity conditions, and execution constraints jointly shape index price movements. This study develops an empirical‐wavelet‐and‐Fourier time‐frequency (EFT) feature block integrated with a ConvTransformer ...
Yi Chen, Ranzhe Jing, Qiang Li
wiley   +1 more source

Building an Online Learning Model Through a Dance Recognition Video Based on Deep Learning

Информатика и автоматизация
Jumping motion recognition via video is a significant contribution because it considerably impacts intelligent applications and will be widely adopted in life. This method can be used to train future dancers using innovative technology. Challenging poses
Nguyen Viet Hung, Thang Quang Loi, Nguyen Hai Binh, Nguyen Thi Thuy Nga, Truong Thu Huong, Duc Lich Luu   +5 more
doaj   +1 more source