Template-Type: ReDIF-Article 1.0 Author-Name: Dadabada Pradeepkumar Author-X-Name-First: Dadabada Author-X-Name-Last: Pradeepkumar Author-Name: Vadlamani Ravi Author-X-Name-First: Vadlamani Author-X-Name-Last: Ravi Title: Financial time series prediction: an approach using motif information and neural networks Abstract: Financial time series prediction is an important and complex problem as well. This paper presents an approach to predict financial time series using time series motifs and artificial neural network (ANN) in tandem. A time series motif is a frequently appearing approximate pattern in a given time series. In the proposed approach, first, extreme points-clustering (EP-C) algorithm detects significant motifs. Later, ANN uses motif information to yield accurate predictions. Three ANNs namely multi-layer perceptron (MLP), general regression neural network (GRNN), and group method for data handling (GMDH) are employed. The proposed Motif+GMDH hybrid outperformed both Motif+MLP hybrid and Motif+ GRNN hybrid on three financial time series including exchange rates of both EUR/USD and INR/USD, and crude oil price (USD). Further, we compared the results of the motif-based hybrids with that of the three ANNs without motif information. We found that Motif+MLP hybrid outperformed plain MLP in all datasets statistically at 1% level of significance. Journal: Int. J. of Data Science Pages: 79-109 Issue: 1 Volume: 5 Year: 2020 Keywords: financial time series prediction; motif; MLP; multi-layer perceptron; GRNN; general regression neural network; GMDH; group method for data handling. File-URL: http://www.inderscience.com/link.php?id=109489 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdsci:v:5:y:2020:i:1:p:79-109 Template-Type: ReDIF-Article 1.0 Author-Name: Liming Xie Author-X-Name-First: Liming Author-X-Name-Last: Xie Title: Statistical analysis of fatal crash in Michigan using more than two time series models Abstract: This paper is to analyse Michigan fatal crash (MFC) in 1974-2014 as time series data using auto regressive integrated moving average (ARIMA) (0,0,1)-GARCH models to predict future values and trends. The author would like to use the heteroskedasticity from the object, such as the rates of incidence, is tested by the autoregressive conditional heteroscedasticity (ARCH) or generalised autoregressive conditional heteroscedasticity (GARCH). The best model of ARCH is to measure the volatility of the MFC so that the future values are predicted. Both ARIMA and ARCH or GARCH models are used to predict future values. The results suggest that GARCH modelling clinch the dynamic change of variance exactly. It suggests that the ARIMA-ARCH/GARCH hybrid modelling is the best method to predict the ahead values of covering the heteroskedastic original objects. Finally, using both ARCH/GARCH forecasting models to predict the future values and the trend of MFC. The results show downward trends. Journal: Int. J. of Data Science Pages: 26-40 Issue: 1 Volume: 5 Year: 2020 Keywords: MFC; Michigan fatal crash; dynamic change; heteroscedasticity; ARIMA; autoregressive integrated moving average; ARCH; autoregressive conditional heteroscedasticity; GARCH; generalised autoregressive conditional heteroscedasticity; forecast. File-URL: http://www.inderscience.com/link.php?id=109490 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdsci:v:5:y:2020:i:1:p:26-40 Template-Type: ReDIF-Article 1.0 Author-Name: M. Sreerama Murty Author-X-Name-First: M. Sreerama Author-X-Name-Last: Murty Author-Name: N. Naga MalleswaraRao Author-X-Name-First: N. Naga Author-X-Name-Last: MalleswaraRao Title: Loading, searching and retrieving data from local data nodes on HDFS Abstract: The loading and searching the data from data with in local data nodes by using the Hadoop environment. In general the loading and searching data by using a query is more complex, because the capacity of the dataset may large. We propose a technique to handle the data in local nodes without overlapping and data retrieved by script. The main task of the query is to store the information on distributed environment and searching the without any delay. Here we define the script to avoid the redundancy of the duplicate while searching and loading the data in dynamic mechanism. And also provide the Hadoop file system in distributed environment. The apache script is used to loading and searching the information instead of the SQL mechanism. We improve the performance of query execution and graph theory. The query can split into three parts to search the data individually and combined the results in execution. Here we used the replica concept to store the data at time of executing query in Hadoop file system. The script is executed on the locating environment of Hadoop file system. Journal: Int. J. of Data Science Pages: 41-52 Issue: 1 Volume: 5 Year: 2020 Keywords: HDFS; Hadoop distributed file system; replica; local; distributed; capacity; SQL; redundancy. File-URL: http://www.inderscience.com/link.php?id=109495 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdsci:v:5:y:2020:i:1:p:41-52 Template-Type: ReDIF-Article 1.0 Author-Name: Farid Kadri Author-X-Name-First: Farid Author-X-Name-Last: Kadri Author-Name: Kahina Abdennbi Author-X-Name-First: Kahina Author-X-Name-Last: Abdennbi Title: RNN-based deep-learning approach to forecasting hospital system demands: application to an emergency department Abstract: In recent years the management of patient flow is one of the main challenges faced by many hospital establishments, in particular emergency departments (EDs). Increasing number of ED demands may lead to ED overcrowding. One approach to alleviate such problems is to predict patient attendances in order to help ED managers to make suitable decisions. Existing regression and time series such as ARIMA models are mainly linear and cannot describe the stochastic and non-linear nature of time series data. In recent years, recurrent neural networks (RNNs) have been applied as novel alternatives for prediction in various domains. In this paper we propose an RNN deep learning based approach for predicting ED demands. The experiments were carried out on a real database collected from the pediatric emergency department (PED) in Lille regional hospital center, France. The RNN-based deep learning approach was shown to provide a useful tool for predicting ED admissions. Journal: Int. J. of Data Science Pages: 1-25 Issue: 1 Volume: 5 Year: 2020 Keywords: ED demands; management of patient flow; ED overcrowding; forecasting ED demands; prediction models; machine learning and deep learning; RNNs; recurrent neural networks; RNN-LSTM; RNN-GRU. File-URL: http://www.inderscience.com/link.php?id=109497 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdsci:v:5:y:2020:i:1:p:1-25 Template-Type: ReDIF-Article 1.0 Author-Name: Ramalingam Shanmugam Author-X-Name-First: Ramalingam Author-X-Name-Last: Shanmugam Title: What do angles of cornea curvature reveal? a new (Sinusoidal) probability density function with statistical properties assists Abstract: This paper introduces a new probability distribution with statistical properties. It is named Sinusoidal probability distribution (SPD). The paper demonstrates an approach to analyse cornea angles' curvature as it happened among the 23 patients in a glaucoma clinic. Expressions are derived for survival and odds functions, their tipping points, its convexity, Q-Q plotting positions, variance-mean relation, heterogeneity among cornea patients, vitality function, total value at risk, past life function, entropy, hazard, inverse, and mean functions. The minimum and maximum sample values are shown to be the maximum likelihood estimators of the parameters of SPD. The joint probability density function for the lower and upper record values of a sample from SPD with their correlation function is derived and utilised to better understand the implications of the measured cornea angles. A few comments are made in the end to further advance the research in cornea illness. Journal: Int. J. of Data Science Pages: 53-78 Issue: 1 Volume: 5 Year: 2020 Keywords: survival function; new probability density function; mean-variance relation; glaucoma incidences; cornea; curvature; odd function; Q-Q plot; heterogeneity; value at risk; entropy. File-URL: http://www.inderscience.com/link.php?id=109498 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdsci:v:5:y:2020:i:1:p:53-78