Çok Katmanlı Yapay Sinir Ağları Yöntemi ile Altın Fiyatlarının Tahmini

Söylemez, Yakup

doi:10.17233/sosyoekonomi.2020.04.13

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…Thanks to this method, they accelerate the manner of information training and might examine extra complicated information styles in a greater whole way [24]. Before talking about Deep Learning techniques used for Forecasting Time Series, it's miles beneficial to keep in mind that the maximum classical Machine Learning models which are used to resolve this hassle are ARIMA models and additionally exponential smoothing [25]. The cryptocurrency marketplace is one of the quickest developing in the global and is taken into consideration as one of the maximum risky markets for transactions.…”

Section: Related Workmentioning

confidence: 99%

A Mechanism for Bitcoin Price Forecasting using Deep Learning

Ateeq,

Zarooni,

Rehman

et al. 2023

IJACSA

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Researchers and investors have recently become interested in forecasting the cryptocurrency price forecasting but the most important currency can take that it's the bitcoin exchange rate. Some researchers have aimed at leveraging the technical and financial characteristics of Bitcoin to create predictive models, while others have utilized conventional statistical methods to explain these factors. This article explores the LSTM model for forecasting the value of bitcoins using historical bitcoin price series. Predict future bitcoin prices by developing the most accurate LSTM forecasting model, building an advanced LSTM forecasting model (LSTM-BTC), and comparing past bitcoin prices. This is the second step, if looking at the end of the model, it has very high accuracy in predicting future prices. The performance of the proposed model is evaluated using five different datasets with monthly, weekly, daily, hourly, and minute-by-minute bitcoin price data with total records from January 1, 2021, to March 31, 2022. The results confirm the better forecasting accuracy of the proposed model using LSTM-BTC. The analysis includes square error MSE, RMSE, MAPE, and MAE of bitcoin price forecasting. Compared to the conventional LSTM model, the suggested LSTM-BTC model performs better. The contribution made by this research is to present a new framework for predicting the price of Bitcoin that solves the issue of choosing and evaluating input variables in LSTM without making firm data assumptions. The outcomes demonstrate its potential use in applications for industry forecasting, including different cryptocurrencies, health data, and economic time.

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Section: Related Workmentioning

confidence: 99%

A Mechanism for Bitcoin Price Forecasting using Deep Learning

Ateeq,

Zarooni,

Rehman

et al. 2023

IJACSA

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“…The main purpose of the MLPNN method is to minimize the error between the expected output of the network and the output it produces. During the training, both the inputs and the (expected) outputs that should be produced against those inputs are shown in these networks (Soylemez 2020). Samples are applied to the input layer, processed in hidden layers, and outputs are obtained from the output layer (Selcuk 2020).…”

Section: Datasetmentioning

confidence: 99%

Classification of Prostate Cancer and Determination of Related Factors with Different Artificial Neural Network

ÇİÇEK

Küçükakçali

2020

Middle Black Sea Journal of Health Science

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Objective: In this study, it is aimed to classify prostate cancer, compare the predictions of these two models and determine the factors associated with the disease by applying Multilayer Perceptron Neural Network (MLPNN) and Radial-Based Function Neural Network (RBFNN) methods on the open access Prostate cancer dataset. Methods: In this study, the dataset named "Prostate Cancer Data Set" was used by obtaining from https://www.kaggle.com/sajidsaifi/prostate-cancer address. To classify prostate cancer, MLPNN and RBFNN methods, which are artificial neural network models, is used. The classification performance of the models was evaluated with the sensitivity, specificity, accuracy, negative predictive value and positive predictive value, which are among the classification performance metrics. Prostate cancer related factors were estimated by using MLPNN and RBFNN models. Results: With the applied MLPNN model, performance metric values were obtained as AUC 0.937, Sensitivity 100%, accuracy 92.5%, Selectivity 84.6%, Positive predictive value 87.5% and Negative predictive value 100%. With the RBFNN model, the performance metric values were obtained as AUC 0.921, Sensitivity 83.3%, accuracy 86.6%, Selectivity 91.6%, Positive predictive value 93.7% and Negative predictive value 78.5%. When the effects of variables in the dataset in this study on prostate cancer are examined; The three most important variables for the MLPNN model were obtained as perimeter, area and compactness, respectively. For the RBFNN model, the three most important variables were obtained as perimeter, area and compactness, respectively. Conclusion: It was seen that MLPNN and RBFNN models used in this study gave successful predictions in the classification of prostate cancer. In addition, estimating the significance values of factors associated with the disease with these classification models made it different from similar studies with the same dataset.

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“…Price prediction models based on neural networks, such as recurrent neural networks (RNN) [ 5 ], convolutional neural networks (CNN), multilayer perceptron (MLP) [ 6 , 7 ], and long short-term memory (LSTM) [ 8 ], have proven to be the most widely used among machine learning approaches in recent years. For the period between March 11, 2014, and March 31, 2019, Soylemez used a multilayer artificial neural network technique to estimate gold prices, utilizing parameters such as Brent oil prices, the VIX index, the Dow Jones index, and the US Dollar index [ 9 ]. In order to forecast the stock prices of selected companies, a long short-term memory model is utilized to analyze the daily stock price movement and returns of different sectors based on their prior values [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Bitcoin and Gold Prices Prediction Method Using an LSTM-P Neural Network Model

Zhang

Zhou

et al. 2022

Computational Intelligence and Neuroscience

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As a result of the fast growth of financial technology and artificial intelligence around the world, quantitative algorithms are now being employed in many classic futures and stock trading, as well as hot digital currency trades, among other applications today. Using the historical price series of Bitcoin and gold from 9/11/2016 to 9/10/2021, we investigate an LSTM-P neural network model for predicting the values of Bitcoin and gold in this research. We first employ a noise reduction approach based on the wavelet transform to smooth the fluctuations of the price data, which has been shown to increase the accuracy of subsequent predictions. Second, we apply a wavelet transform to diminish the influence of high-frequency noise components on prices. Third, in the price prediction model, we develop an optimized LSTM prediction model (LSPM-P) and train it using historical price data for gold and Bitcoin to make accurate predictions. As a consequence of our model, we have a high degree of accuracy when projecting future pricing. In addition, our LSTM-P model outperforms both the conventional LSTM models and other time series forecasting models in terms of accuracy and precision.

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Çok Katmanlı Yapay Sinir Ağları Yöntemi ile Altın Fiyatlarının Tahmini

Cited by 6 publications

References 22 publications

A Mechanism for Bitcoin Price Forecasting using Deep Learning

A Mechanism for Bitcoin Price Forecasting using Deep Learning

Classification of Prostate Cancer and Determination of Related Factors with Different Artificial Neural Network

A Novel Bitcoin and Gold Prices Prediction Method Using an LSTM-P Neural Network Model

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