Curiosity concerning the drop‐size composition of natural rain has arisen from attempts to measure erodibility and infiltration‐capacity by sprinkling small areas of land with artificial rain. The results have been found to be affected by the drop‐size and velocity of the artificial rains applied, and the applicability of such results to conditions of natural rainfall has been thrown in doubt.
This paper presents measurements of the velocities of water‐drops of sizes ranging from one to six mm in diameter, falling in still air from heights of 0.5 meter to 20 meters. A few measurements of raindrop velocities are also reported.
The measurements were undertaken to assist in an understanding of the action of rain, both real and artificial, in eroding soil. The drop‐sizes of rains have also been measured and will be reported separately. All of these studies were carried out at the Hydraulic Laboratory of the National Bureau of Standards as a part of the work of the Soil Conservation Service.
The motivation for this paper is to investigate the use of alternative novel neural network (NN) architectures when applied to the task of forecasting and trading the euro/dollar (EUR/USD) exchange rate, using the European Central Bank (ECB) fixing series with only auto-regressive terms as inputs. This is done by benchmarking four different NN designs representing a higher-order neural network (HONN), a Psi Sigma Network and a recurrent neural network with the classic multilayer perception (MLP) and some traditional techniques, either statistical such as an auto-regressive moving average model, or technical such as a moving average convergence/divergence model, plus a naive strategy. More specifically, the trading performance of all models is investigated in a forecast and trading simulation on the EUR/USD ECB fixing time series over the period 1999-2007 using the last one and half years for out-of-sample testing, an original feature of this paper. We use the EUR/USD daily fixing by the ECB as many financial institutions are ready to trade at this level and it is therefore possible to leave orders with a bank for business to be transacted on that basis. As it turns out, the MLP does remarkably well and outperforms all other models in a simple trading simulation exercise. However, when more sophisticated trading strategies using confirmation filters and leverage are applied, the HONN network produces better results and outperforms all other NN and traditional statistical models in terms of annualized return.confirmation filters, higher-order neural networks, Psi Sigma networks, recurrent neural networks, leverage, multi-layer perception networks, quantitative trading strategies,
The motivation for this paper is to investigate the use of alternative novel neural network architectures when applied to the task of forecasting and trading the Euro/Dollar (EUR/USD) exchange rate. This is done by benchmarking three different neural network designs representing a Higher Order Neural Network (HONN), a Psi Sigma Network and a Recurrent Network (RNN) with three successful architectures, the traditional Multilayer Perceptron (MLP), the Softmax and the Gaussian Mixture (GM) models. More specifically, the trading performance of the six models is investigated in a forecast and trading simulation competition on the EUR/USD time series over a period of 8 years. These results are also benchmarked with more traditional models such as a moving average convergence divergence technical model (MACD), an autoregressive moving average model (ARMA) and a logistic regression model (LOGIT). As it turns out, the MLP, the HONN, the Psi Sigma and the RNN models all do well and outperform the more traditional models in a simple trading simulation exercise. However, when more sophisticated trading strategies using confirmation filters and leverage are applied, the GM network produces remarkable results and outperforms all the other network architectures.Quantitative trading strategies, Volatility modelling, Risk management, Options volatility,
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