Cryptocurrencies Price Index Prediction Using Neural Networks on Bittrex Exchange

2019 ◽  
pp. 648-655
Author(s):  
Phan Duy Hung ◽  
Tran Quang Thinh
Keyword(s):  
Neural Networks ◽  
Price Index

scholarly journals Evaluación del índice económico de Colombia para el período 2020 a 2022 con redes neuronales artificiales

2020 ◽  
Vol 42 (1) ◽  
pp. 25-33
Author(s):  
Valeria Alejandra Bustamante Zuleta ◽  
Hermes Jackson Martinez Navas
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gross Domestic Product ◽  
Price Index ◽  
Consumer Price Index ◽  
Oil Price ◽  
Domestic Product ◽  
Positive Effects ◽  
Market Rate ◽  
Macroeconomic Indicators

This article analyze some of the important macroeconomic indicators in Colombia,such as the Consumer Price Index (CPI), the Gross Domestic Product (GDP), the Representative Market Rate (TRM), the Oil Price (BRENT and WIT) and COLCAP. The objective is to study Colombia's economic.The analysis were obtained with artificial neural networks on Colombian indicators data for the period 2001 to 2018 of the National Administrative Department of Statistics (DANE) and Bloomberg. Concluding, for Colombia, the last two cases are highly favorable for the economy, because they will generate a greater influx of dollars, allowing positive effects on the domestic product and the consumer price index.

A THREE-STEP COMBINED GENETIC PROGRAMMING AND NEURAL NETWORKS METHOD OF FORECASTING THE S&P/CASE-SHILLER HOME PRICE INDEX

2013 ◽  
Vol 12 (01) ◽  
pp. 1350001 ◽  
Author(s):  
MAK KABOUDAN ◽  
MARK CONOVER
Keyword(s):  
Neural Networks ◽  
Genetic Programming ◽  
San Francisco ◽  
Price Index ◽  
Combined Method ◽  
Second Stage ◽  
Third Stage ◽  
Multi Agent ◽  
Stage Process ◽  
Two Stages

Forecasts of the San Diego and San Francisco S&P/Case-Shiller Home Price Indices through December 2012 are obtained using a multi-agent system that utilizes January, 2002–June, 2011 data. Agents employ genetic programming (GP) and neural networks (NN) in a three-stage process to produce fits and forecasts. First, GP and NN compete to provide independent predictions. In the second stage, they cooperate by fitting the first-stage competitor's residuals. Outputs from the first two stages then become inputs to produce two final GP and NN outputs. The NN output from the third stage using the combined method produces improved forecasts over the 3-stage GP method as well as those produced by either method alone. The proposed methodology serves as an example of how combining more than one estimation/forecasting technique may lead to more accurate forecasts.

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