scholarly journals Modeling and Forecasting Cryptocurrency Closing Prices with Rao Algorithm-Based Artificial Neural Networks: A Machine Learning Approach

FinTech ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 47-62
Author(s):  
Sanjib Kumar Nayak ◽  
Sarat Chandra Nayak ◽  
Subhranginee Das
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Financial Time Series ◽  
Optimal Parameters ◽  
Financial Time ◽  
Good Investment ◽  
Machine Learning Approach ◽  
Modeling And Forecasting ◽  
The Mean ◽  
Artificial Neural

Artificial neural networks (ANNs) are suitable procedures for predicting financial time series (FTS). Cryptocurrencies are good investment assets; therefore, the effective prediction of cryptocurrencies has become a trending area of research. Capturing inherent uncertainties associated with cryptocurrency FTS with conventional methods is difficult. Though ANNs are the better alternative, fixing the optimal parameters of ANNs is a tedious job. This article develops a hybrid ANN through Rao algorithm (RA + ANN) for the effective prediction of six popular cryptocurrencies such as Bitcoin, Litecoin, Ethereum, CMC 200, Tether, and Ripple. Six comparative models such as GA + ANN, PSO + ANN, MLP, SVM, LSE, and ARIMA are developed and trained in a similar way. All these models are evaluated through the mean absolute percentage of error (MAPE) and average relative variance (ARV) metrics. It is found that the proposed RA + ANN generated the lowest MAPE and ARV values, statistically different as compared with existing methods mentioned above, and hence can be recommended as a potential financial instrument for predicting cryptocurrencies.

MEMORY IN FINANCIAL TIME SERIES: FROM INVESTOR BEHAVIOR TO ARTIFICIAL NEURAL NETWORKS

2012 ◽  
Author(s):  
M. TERESA SORROSAL-FORRADELLAS ◽  
M. GLÒRIA BARBERÀ-MARINÉ ◽  
LISANA B. MARTINEZ ◽  
MARÍA-JOSÉ GARBAJOSA-CABELLO
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Financial Time Series ◽  
Investor Behavior ◽  
Financial Time ◽  
Artificial Neural

scholarly journals Financial time series forecasting using Artificial Neural Networks

2019 ◽  
Vol 15 (1) ◽  
pp. 105-122
Author(s):  
Roberto Gallardo Del Ángel
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Financial Time Series ◽  
Time Series Forecasting ◽  
Financial Time ◽  
Artificial Neural

Este documento contiene una predicción financiera utilizando Redes Neuronales Artificiales. Hacemos nuestro análisis utilizando el algoritmo de Backpropagation tradicional y luego Backpropagation Resiliente para estimar los pesos en las redes. El uso del algorithm de Bacpropagation Resiliente permite resolver el problema de la determinación de la tasa de aprendizaje. Ambos algoritmos son bastante consistentes y arrojan predicciones similares. Analizamos seis índices principales de los mercados bursátiles de Europa, Asia y América del Norte para generar índices de aciertos que puedan compararse entre mercados. Usamos precios de cierre diarios para construir una variable de dependiente para dirigir el aprendizaje (aprendizaje supervisado) y una matriz de variables de características construidas utilizando indicadores de análisis técnico. El rango de datos de la serie de tiempo va desde Enero de 2000 a Junio de 2019, un periodo de grandes fluctuaciones debido a mejoras en la tecnología de la información y una alta movilidad de capital. En lugar de la predicción en sí misma, el objetivo científico es evaluar la importancia relativa de las variables independientes que permiten la predicción. Utilizamos dos medidas de contribución utilizadas en la literatura para evaluar la relevancia de cada variable para los seis mercados financieros analizados. Descubrimos que estas medidas no siempre son consistentes, por lo que construimos una medida de contribución simple que le da a cada peso una interpretación geométrica. Proporcionamos algunas pruebas de que la tasa de cambio (ROC) es la herramienta de predicción más útil para cuatro índices generales, con las excepciones siendo el índice Hang Sheng y EU50, en donde el fastK es el más destacado.

Artificial Neural Networks Estimation for Thicknesses of Multilayer Nano-Scale Films

2019 ◽  
Vol 962 ◽  
pp. 41-48
Author(s):  
Tzong Daw Wu ◽  
Jiun Shen Chen ◽  
Ching Pei Tseng ◽  
Cheng Chang Hsieh
Keyword(s):  
Thin Films ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Film Growth ◽  
Mean Squared Error ◽  
Squared Error ◽  
Thin Film Optics ◽  
The Mean ◽  
Artificial Neural

This study presents a real-time method for determining the thickness of each layer in multilayer thin films. Artificial neural networks (ANNs) were introduced to estimate thicknesses from a transmittance spectrum. After training via theoretical spectra which were generated by thin-film optics and modified by noise, ANNs were applied to estimate the thicknesses of four-layer nanoscale films which were TiO2, Ag, Ti, and TiO2 thin films assembled sequentially on polyethylene terephthalate (PET) substrates. The results reveal that the mean squared error of the estimation is 2.6 nm2, and is accurate enough to monitor film growth in real time.

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