Results 121 to 130 of about 48,097 (299)

Hybrid Temporal Autoencoder and Similarity Matching for Low Aggregation Level Long Time Series Forecasting

Journal of Forecasting, EarlyView.
ABSTRACT Deep learning‐based long time series forecasting (LTSF) has achieved high accuracy by effectively capturing the underlying trends, seasonality, and temporal dependencies within time series data. However, at the individual entity level, termed the low aggregation level (LAL), intermittency, irregularity, and data sparsity undermine the ...
Hanbyeol Park, Sunghyun Sim, Kikun Park, Yongjae Lee, Eunhee Park, Hyerim Bae   +5 more
wiley   +1 more source

Problemas y desafíos de la minería de oro artesanal y en pequeña escala en Colombia

Revista Facultad de Ciencias Económicas, 2016
El objetivo del presente artículo de reflexión es analizar brevemente las condiciones sociales, políticas, económicas, tecnológicas y ambientales de la minería artesanal y en pequeña escala (MAPE) del oro en Colombia.
Freddy Hernán Pantoja Timarán, Sebastián David Pantoja Barrios   +1 more
doaj  

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

Implementation of the CNN-LSTM Hybrid Model in Predicting Bitcoin Price Fluctuations

Jurnal Teknologi dan Manajemen Informatika
Digital financial systems of today face formidable obstacles from the extreme price volatility and unpredictability of Bitcoin. Data cleaning, Min-Max normalization, and sequence creation with a sliding window were performed on the daily BTC-USD ...
Candra Wibowo, Ronsen Purba, Muhammad Fermi Pasha   +2 more
doaj   +1 more source

Temporal Robustness in Cross‐Country Gross Domestic Product Forecasting: Evidence From Classical, Regime‐Switching, and Ensemble Models

Journal of Forecasting, EarlyView.
ABSTRACT This study evaluates whether the relative performance of gross domestic product forecasting models is temporally robust across economies and changing macroeconomic conditions. Using annual real gross domestic product data from the World Bank for 177 separately reported economies and territories, we conduct a common‐vintage pseudo‐out‐of‐sample
Achilleas Tampouris, Chaido Dritsaki
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

Pendekatan Model Fuzzy untuk Pendugaan Bobot Badan Kerbau Lumpur Berdasarkan Lingkar Dada dan Panjang Badan

, 2013
Body weight of a swamp buffalo can be determined by measuring the buffalo weight directly or indirectly. Measuring the buffalo weight directly is relatively difficult. Measuring the weight indirectly can be performed using a conventional formula or fuzzy
Sista, Ito Hadi
core  

A Coupled AquaCrop–Richards Model for Improved Crop Yield Prediction Through Physically Based Soil Water Dynamics

Irrigation and Drainage, EarlyView.
ABSTRACT Crop modelling is essential for agricultural water management but often relies on simplified water balance routines that limit representation of soil moisture dynamics. To address this limitation, we developed a coupled model that integrates the 1‐D Richards equation, solved using a finite difference method into the FAO AquaCrop.
Krishna Panthi, Vidya Samadi, Carlos Toxtli   +2 more
wiley   +1 more source

Common‐Mode Rejection Shifted‐Excitation Raman Difference Spectroscopy (CMR‐SERDS) Preserves Broad Structure Predictive of Soil Organic Carbon

Journal of Raman Spectroscopy, EarlyView.
Common‐mode rejection (CMR) is introduced as a physics‐motivated preprocessing method for shifted excitation Raman difference spectroscopy (SERDS) that removes the shared background of paired measurements while preserving the noncommon excitation‐dependent component. Applied to more than 900 North American soil samples, CMR improves soil organic carbon
Mahsa Zarei, Miayan Larose, Natalia V. Solomatova, Sadegh Shokatian, Edward Grant   +4 more
wiley   +1 more source

Forecasting the Financial Times Stock Exchange Bursa Malaysia Kuala Lumpur Composite Index Using Geometric Brownian Motion

Journal of Computing Research and Innovation, 2018
In Malaysia, Financial Times Stock Exchange (FTSE) of Bursa Malaysia Kuala Lumpur Composite Index (FBMKLCI) provides charts, companies’ profile and other market data to help the local and foreign investors to make decisions involving their investments ...
Teoh Yeong Kin, Suzanawati Abu Hasan, Nashni Hamdan   +2 more
doaj