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Neural Networks - originally inspired from Neuroscience - provide powerful models for statistical data analysis. Their most prominent feature is their ability to "learn" dependencies based on a finite number of observations.
The goal of this article is to provide a practical introductory guide to neural networks for forecasting financial time series data using Azure Deep Learning Virtual Machine. A multiple step ...
23 Μαρ 2021 · Our study uses the grey relational analysis (GRA) and artificial neural network (ANN) models for the prediction of consumer exchange-traded funds (ETFs). We apply eight variables, including the put/call ratio, the EUR/USD exchange rate, the volatility index, the Commodity Research Bureau Index (CRB), the short-term trading index, the New York ...
Neural Networks – originally inspired from Neuroscience – provide powerful models for statistical data analysis. Their most prominent feature is their ability to “learn” dependencies based on a finite number of observations.
Then we gave an overview of existing economic applications of neural networks, where we distinguished between three types: Classification of economic agents, time series prediction and the modelling of bounded rational agents.
Accordingly, we explore the use of neural networks in forecasting macroeconomic indicators, focusing speci cally on unemployment. The models we present improve on the widely-cited SPF forecast in near-term prediction.
7 Μαΐ 2022 · This paper aims to forecast: (i) the closing price of eight stock market indexes; and (ii) the closing price of six currency exchange rates related to the USD, using the RNNs model and its variants: the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU).
