FORECASTING DAILY FOREIGN EXCHANGE RATE IN INDIA WITH ARTIFICIAL NEURAL NETWORK

2003 ◽  
Vol 48 (02) ◽  
pp. 181-199 ◽  
Author(s):  
CHAKRADHARA PANDA ◽  
V. NARASIMHAN
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Exchange Rate ◽  
Random Walk ◽  
Autoregressive Model ◽  
Out Of Sample ◽  
One Step ◽  
Artificial Neural ◽  
The One ◽  
Better Than

This study compares the efficiency of a non-linear model called artificial neural network with linear autoregressive and random walk models in the one-step-ahead prediction of daily Indian rupee/US dollar exchange rate. We find that neural network and linear autoregressive models outperform random walk model in in-sample and out-of-sample forecasts. The in-sample forecasting of neural network is found to be better than that of linear autoregressive model. As far as out-of-sample forecasting is concerned, the results are mixed and we do not find a "winner" model between neural network and linear autoregressive model. However, neural network is able to improve upon the linear autoregressive model in terms of sign predictions. In addition to this, we also find that the number of input nodes has greater impact on neural network's performance than the number of hidden nodes.

Artificial neural network-based seismic detector

1995 ◽  
Vol 85 (1) ◽  
pp. 308-319 ◽  
Author(s):  
Jin Wang ◽  
Ta-Liang Teng
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Input Data ◽  
Seismic Event ◽  
Signal To Noise Ratio ◽  
Type B ◽  
Earthquake Detection ◽  
Seismic Event Detection ◽  
Artificial Neural ◽  
Better Than

Abstract An artificial neural network-based pattern classification system is applied to seismic event detection. We have designed two types of Artificial Neural Detector (AND) for real-time earthquake detection. Type A artificial neural detector (AND-A) uses the recursive STA/LTA time series as input data, and type B (AND-B) uses moving window spectrograms as input data to detect earthquake signals. The two AND's are trained under supervised learning by using a set of seismic recordings, and then the trained AND's are applied to another set of recordings for testing. Results show that the accuracy of the artificial neural network-based seismic detectors is better than that of the conventional algorithms solely based on the STA/LTA threshold. This is especially true for signals with either low signal-to-noise ratio or spikelike noises.

scholarly journals Recurrent Artificial Neural Networks (RANN) for forecasting of forward interest rates

2000 ◽  
Vol 31 (4) ◽  
pp. 137-140
Author(s):  
Amine Bensaid ◽  
Bouchra Bouqata ◽  
Ralph Palliam
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Interest Rates ◽  
Recursive Method ◽  
Alternative Technique ◽  
Forward Rates ◽  
Artificial Neural ◽  
The One ◽  
Recurrent Artificial Neural Networks

There are numerous methods for estimating forward interest rates as well as many studies testing the accuracy of these methods. The approach proposed in this study is similar to the one in previous works in two respects: firstly, a Monte Carlo simulation is used instead of empirical data to circumvent empirical difficulties: and secondly, in this study, accuracy is measured by estimating the forward rates rather than by exploring bond prices. This is more consistent with user objectives. The method presented here departs from the others in that it uses a Recurrent Artificial Neural Network (RANN) as an alternative technique for forecasting forward interest rates. Its performance is compared to that of a recursive method which has produced some of the best results in previous studies for forecasting forward interest rates.

EXTENDED DAILY EXCHANGE RATES FORECASTS USING WAVELET TEMPORAL RESOLUTIONS

2005 ◽  
Vol 01 (01) ◽  
pp. 79-107 ◽  
Author(s):  
MAK KABOUDAN
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Exchange Rate ◽  
Random Walk ◽  
Genetic Programming ◽  
Exchange Rates ◽  
Us Dollar ◽  
Artificial Neural ◽  
Japanese Yen ◽  
The One

Applying genetic programming and artificial neural networks to raw as well as wavelet-transformed exchange rate data showed that genetic programming may have good extended forecasting abilities. Although it is well known that most predictions of exchange rates using many alternative techniques could not deliver better forecasts than the random walk model, in this paper employing natural computational strategies to forecast three different exchange rates produced two extended forecasts (that go beyond one-step-ahead) that are better than naïve random walk predictions. Sixteen-step-ahead forecasts obtained using genetic programming outperformed the one- and sixteen-step-ahead random walk US dollar/Taiwan dollar exchange rate predictions. Further, sixteen-step-ahead forecasts of the wavelet-transformed US dollar/Japanese Yen exchange rate also using genetic programming outperformed the sixteen-step-ahead random walk predictions of the exchange rate. However, random walk predictions of the US dollar/British pound exchange rate outperformed all forecasts obtained using genetic programming. Random walk predictions of the same three exchange rates employing raw and wavelet-transformed data also outperformed all forecasts obtained using artificial neural networks.

Use of an Artificial Neural Network for Data Analysis in Clinical Decision-Making: The Diagnosis of Acute Coronary Occlusion

1990 ◽  
Vol 2 (4) ◽  
pp. 480-489 ◽  
Author(s):  
William G. Baxt
Keyword(s):  
Neural Network ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Artificial Neural Network ◽  
Coronary Occlusion ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Acute Coronary Occlusion ◽  
Artificial Neural ◽  
Better Than

A nonlinear artificial neural network trained by backpropagation was applied to the diagnosis of acute myocardial infarction (coronary occlusion) in patients presenting to the emergency department with acute anterior chest pain. Three-hundred and fifty-six patients were retrospectively studied, of which 236 did not have acute myocardial infarction and 120 did have infarction. The network was trained on a randomly chosen set of half of the patients who had not sustained acute myocardial infarction and half of the patients who had sustained infarction. It was then tested on a set consisting of the remaining patients to which it had not been exposed. The network correctly identified 92% of the patients with acute myocardial infarction and 96% of the patients without infarction. When all patients with the electrocardiographic evidence of infarction were removed from the cohort, the network correctly identified 80% of the patients with infarction. This is substantially better than the performance reported for either physicians or any other analytical approach.

Artificial Neural Network Assistant (ANNA) for Continuous Auditing and Monitoring of Financial Data

2007 ◽  
Vol 4 (1) ◽  
pp. 29-45 ◽  
Author(s):  
Eija Koskivaara ◽  
Barbro Back
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Function Approximation ◽  
Financial Data ◽  
Expectation Values ◽  
Continuous Auditing ◽  
Computer Screen ◽  
One Step ◽  
Artificial Neural

This paper presents the development and applications of ANNA—Artificial Neural Network Assistant (ANNA) for continuous auditing and monitoring of financial data. The prototype presented here provides automatically six types of expectation values for account values and compares them to the actual values. In this study, ANNA is used for analyzing monthly account values. ANNA's predictions or account expectations are based on function approximation (modeling) capability from the previous years' monthly account values. ANNA uses a flexible one-step-ahead prediction model. The results are illustrated both by a graph on the computer screen and in the tables. The evaluation of results show that artificial neural networks technology is competitive compared to other methods used in this study.

scholarly journals The Analysis of Performace Model Tiered Artificial Neural Network for Assessment of Coronary Heart Disease

2017 ◽  
Vol 7 (4) ◽  
pp. 2183
Author(s):  
Wiharto Wiharto ◽  
Harianto Herianto ◽  
Hari Kusnanto
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Artificial Neural Network ◽  
Heart Disease ◽  
Assessment Model ◽  
Model Assessment ◽  
Marquardt Algorithm ◽  
One Step ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<p>The assessment model of coronary heart disease is so much developed in line with the development of information technology, particularly the field of artificial intelligence. Unfortunately, the assessment models developed mostly do not use such an approach made by the clinician, the tiered approach. This study aims to analyze the performance of a tiered model assessment. The method used for each level is, preprocessing, building architecture artificial neural network (ANN), conduct training using the Levenberg-Marquardt algorithm and one step secant, as well as testing the system. The study is divided into the terms of the stages in the examination procedure. The test results showed the influence of each level, both when the output level of the previous positive or negative, were tested back at the next level. The performance evaluation may indicate that the top level provides performance improvement and or reinforce the previous level. </p>

scholarly journals Improvement of methods of hydrological forecasting using geoinformation technologies

2021 ◽  
Vol 2131 (3) ◽  
pp. 032069
Author(s):  
A Zueva ◽  
V Shamova ◽  
T Pilipenko
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Activation Function ◽  
Water Levels ◽  
Electronic Map ◽  
Hydrological Forecasting ◽  
Ob River ◽  
Network Methods ◽  
One Step ◽  
Artificial Neural

Abstract This article discusses the possibility of improving hydrological forecasting methods based on a neural network. The hydrological series, its importance and forecasting features are considered. For hydrological forecasting using the MapInfoProfessional geoinformation system, an electronic map has been developed containing information about the rivers of Russia, as well as gauging stations on the Ob River. The electronic map is the basis for creating a module for short-term hydrological forecasting based on an artificial neural network. The features of a neural network, methods of its training and implementation are considered. The developed artificial neural network is a layer of neurons with a linear activation function and a delay line at the input. To predict the levels of hydrological series, real water levels at gauging stations of the Ob River in the Novosibirsk region will be used. The developed module and its capabilities have been tested. The study was carried out on the basis of models of hydrological series, as well as on the basis of levels of real hydrological series. Based on the study, dependence of the root-mean-square error on the number of previous values of series was revealed. The study also shows that it is possible to use a neural network for the current one-step forecasting of levels of hydrological series in conditions of insufficient information about the runoff region and its characteristics.

scholarly journals SEIRDQ: A COVID-19 case projection modeling framework using ANN to model quarantine

2021 ◽  
Author(s):  
Harish Chandra ◽  
Xianwei Meng ◽  
Arman Margaryan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Sources ◽  
Modeling Framework ◽  
Novel Approach ◽  
Out Of Sample ◽  
Artificial Neural ◽  
Model Versus ◽  
Artificial Neural Network Ann ◽  
The Impact

We propose and implement a novel approach to model the evolution of COVID-19 pandemic and predict the daily COVID-19 cases (infected, recovered and dead). Our model builds on the classical SEIR-based framework by adding additional compartments to capture recovered, dead and quarantined cases. Quarantine impacts are modeled using an Artificial Neural Network (ANN), leveraging alternative data sources such as the Google mobility reports. Since our model captures the impact of lockdown policies through the quarantine functions we designed, it is able to model and predict future waves of COVID-19 cases. We also benchmark out-of-sample predictions from our model versus those from other popular COVID-19 case projection models.

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