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THE COMPARISON OF ARIMA AND RNN FOR FORECASTING GOLD FUTURES CLOSING PRICES
BarekengIn the financial markets, accurately forecasting the closing prices of gold futures is crucial for investors and analysts. Traditional methods like ARIMA (Autoregressive Integrated Moving Average) have been widely used for this purpose, particularly for ...
Windy Ayu Pratiwi +4 more
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Low Precision RNNs: Quantizing RNNs Without Losing Accuracy
CoRR, 2017 Similar to convolution neural networks, recurrent neural networks (RNNs) typically suffer from over-parameterization. Quantizing bit-widths of weights and activations results in runtime efficiency on hardware, yet it often comes at the cost of reduced accuracy.
Supriya Kapur +2 more
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Modelling Time Series Data for Stock Prices Prediction Using Bidirectional Long Short-Term Memory
KnowbaseThe dynamic nature of stock markets, characterized by intricate patterns and sudden fluctuations, poses significant challenges to accurate price prediction. Traditional analytical methods are often unable to capture this complexity. This requires the use
Yenie Syukriyah, Adi Purnama
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W-RNN: News text classification based on a Weighted RNN
CoRR, 2019 7 pages, 10 ...
Dan Wang, Jibing Gong, Yaxi Song
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Time-Series Forecasting of the Pazarcık Earthquake Using LSTM, Transformer and RNN Models
Gazi Üniversitesi Fen Bilimleri DergisiThe Earth's internal structure and mitigating seismic hazards are very important for understanding for earthquake prediction and seismic wave analysis.
Seda Şahin , Emine Çankaya
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SENTIMENT ANALYSIS WITH LONG-SHORT TERM MEMORY (LSTM) AND GATED RECURRENT UNIT (GRU) ALGORITHMS
Barekeng, 2023 Sentiment analysis is a form of machine learning that functions to obtain emotional polarity values or data tendencies from data in the form of text. Sentiment analysis is needed to analyze opinions, sentiments, reviews, and criticisms from someone for a
Muhammad Nazhif Abda Putera Khano +3 more
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DB-RNN: An RNN for Precipitation Nowcasting Deblurring
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingPrecipitation nowcasting based on artificial intelligence has garnered widespread attention in the meteorological and computer communities in recent years. While new models are continuously proposed to refresh the forecasting performance, the problem of gradual blurring of forecast maps as the forecast period extends is still serious.
Zhifeng Ma, Hao Zhang 0016, Jie Liu 0001
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CoRR, 2021 Contemporary wisdom based on empirical studies suggests that standard recurrent neural networks (RNNs) do not perform well on tasks requiring long-term memory. However, precise reasoning for this behavior is still unknown. This paper provides a rigorous explanation of this property in the special case of linear RNNs.
Melikasadat Emami +4 more
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2017 International Joint Conference on Neural Networks (IJCNN), 2017 In this paper we examine learning methods combining the Random Neural Network, a biologically inspired neural network and the Extreme Learning Machine that achieve state of the art classification performance while requiring much shorter training time.
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On‐Chip Photonic Neural Network Architectures
Advanced Optical Materials, EarlyView.This review presents a comprehensive overview of on‐chip photonic neural network architectures, covering key photonic building blocks, representative network types, and emerging applications. Recent advances, implementation challenges, and future directions are examined, highlighting the potential of integrated photonics to enable ultrafast, energy ...
Seokjin Hong +7 more
wiley +1 more source