scholarly journals Rough Set Based Affinity Propagation Model for Prediction of Future Gold Price in Indian Scenario

2019 ◽  
Vol 9 (2) ◽  
pp. 2219-2224
Keyword(s):  
Rough Set ◽  
Arima Model ◽  
Gold Price ◽  
Propagation Model ◽  
Affinity Propagation ◽  
Experimental Result ◽  
Percentage Error ◽  
Support Vector ◽  
Proposed Model ◽  
Gold Silver

In international market, trading of metals has played a vital role. Metal cost might affect the nation’s economy. There are so many base metals available which have been utilized in world trading for construction and manufacturing of goods. Among them gold, silver, platinum, palladium have been treated as precious metals which has economic values. Therefore today’s researchers have concentrated their investigation on metal prediction using diversified algorithms like Auto Regressive Integrated Moving Average (ARIMA), KNN (K-Nearest Neighbor),Artificial Neural Network (ANN) and Support Vector Machine (SVM) etc. In this paper our foremost objective is to predict gold price, so we put our research on this metal. In this work we have employed rough set based affinity propagation algorithm for predicting future gold price and we compared our proposed model with rough set and ARIMA model basing upon the performance measures such as root mean square error (RMSE) and mean absolute percentage error (MAPE). The experimental result shows that the proposed model outperforms rough set and ARIMA model

Towards optimal ATM cash replenishment using time series analysis

2021 ◽  
pp. 1-13
Author(s):  
Muhammad Rafi ◽  
Mohammad Taha Wahab ◽  
Muhammad Bilal Khan ◽  
Hani Raza
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Automatic Teller Machine ◽  
Proposed Model ◽  
Auto Regressive ◽  
Specific Amount

Automatic Teller Machine (ATM) are still largely used to dispense cash to the customers. ATM cash replenishment is a process of refilling ATM machine with a specific amount of cash. Due to vacillating users demands and seasonal patterns, it is a very challenging problem for the financial institutions to keep the optimal amount of cash for each ATM. In this paper, we present a time series model based on Auto Regressive Integrated Moving Average (ARIMA) technique called Time Series ARIMA Model for ATM (TASM4ATM). This study used ATM back-end refilling historical data from 6 different financial organizations in Pakistan. There are 2040 distinct ATMs and 18 month of replenishment data from these ATMs are used to train the proposed model. The model is compared with the state-of- the-art models like Recurrent Neural Network (RNN) and Amazon’s DeepAR model. Two approaches are used for forecasting (i) Single ATM and (ii) clusters of ATMs (In which ATMs are clustered with similar cash-demands). The Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) are used to evaluate the models. The suggested model produces far better forecasting as compared to the models in comparison and produced an average of 7.86/7.99 values for MAPE/SMAPE errors on individual ATMs and average of 6.57/6.64 values for MAPE/SMAPE errors on clusters of ATMs.

scholarly journals Convolutional Neural Network Integrated With Fuzzy Rules for Decision Making in Brain Tumor Diagnosis

2021 ◽  
Vol 15 (4) ◽  
pp. 1-23
Author(s):  
Pham Van Hai ◽  
Samson Eloanyi Amaechi
Keyword(s):  
Neural Networks ◽  
Medical Imaging ◽  
Brain Tumors ◽  
Convolutional Neural Networks ◽  
Fuzzy Rules ◽  
Experimental Result ◽  
Support Vector ◽  
Proposed Model ◽  
Diagnosis And Classification

Conventional methods used in brain tumors detection, diagnosis, and classification such as magnetic resonance imaging and computed tomography scanning technologies are unbridged in their results. This paper presents a proposed model combination, convolutional neural networks with fuzzy rules in the detection and classification of medical imaging such as healthy brain cell and tumors brain cells. This model contributes fully on the automatic classification and detection medical imaging such as brain tumors, heart diseases, breast cancers, HIV and FLU. The experimental result of the proposed model shows overall accuracy of 97.6%, which indicates that the proposed method achieves improved performance than the other current methods in the literature such as [classification of tumors in human brain MRI using wavelet and support vector machine 94.7%, and deep convolutional neural networks with transfer learning for automated brain image classification 95.0%], uses in the detection, diagnosis, and classification of medical imaging decision supports.

scholarly journals Transmission Potential and Forecasting The Number of Coronavirus Disease 2019 in Hubei Province, China

2020 ◽  
Author(s):  
Ke-wei Wang ◽  
Jie Gao ◽  
Hua Wang ◽  
Xiao-long Wu ◽  
Qin-fang Yuan ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Arima Model ◽  
Hubei Province ◽  
Transmission Dynamics ◽  
Percentage Error ◽  
Terminal Phase ◽  
Support Vector ◽  
Temporal And Spatial Distribution ◽  
Svm Model ◽  
Temporal And Spatial

Abstract Background: Coronavirus disease 2019 (COVID-19) was first reported in Wuhan, Hubei province, China. We aimed to describe the temporal and spatial distribution and the transmission dynamics of COVID-19 and to assess whether a hybrid model can forecast the trend of COVID-19 in Hubei Province, China. Method: The data of COVID-19 cases were obtained from the websites of Chinese Center for Disease Control and Prevention, whereas the data on the resident population were obtained from the websites of Hubei Provincial Bureau of Statistics. The temporal and spatial distribution and the transmission dynamics of COVID-19 were described. A combination of autoregressive integrated moving average (ARIMA) and support vector machine was constructed to forecast the trend of COVID-19. Results: A total of 56,062 confirmed COVID-19 cases, which were mainly concentrated in Wuhan, were reported from January 16 to March 16, 2020 in Hubei Province, China. The daily number of confirmed cases exponentially increased to 3,156 before February 4, 2020, fluctuated to 4,823 before February 13, 2020, and then markedly decreased to 1 after March 16, 2020. The highest mean reproduction number R(t) of 9.48 was recorded on January 16, 2020, after which it decreased to 2.15 on February 2, 2020 and further decreased to less than 1 on February 13, 2020. In the modeling stage, the mean square error, mean absolute error, and mean absolute percentage error of the hybrid ARIMA–SVM model decreased by 98.59%, 89.19% and 89.68%, and those of SVM decreased by 98.58%, 87.71%, and 88.94%, respectively, compared with the ARIMA model. Similar results were obtained in the forecasting stage.Conclusion: Public health interventions resulted in the terminal phase of COVID-19 in Hubei province. The hybrid ARIMA–SVM model may be a reliable tool for forecasting the trend of the COVID-19 epidemic.

scholarly journals Forecasting Wind Speed using Artificial Neural Networks – A Case Study of a Potential Location of Saudi Arabia

2020 ◽  
Vol 173 ◽  
pp. 01004
Author(s):  
Yunus Parvej Faniband ◽  
S. M. Shaahid
Keyword(s):  
Neural Networks ◽  
Saudi Arabia ◽  
Renewable Energy ◽  
Wind Speed ◽  
Machine Learning Techniques ◽  
Percentage Error ◽  
Support Vector ◽  
Wind Speed Data ◽  
Proposed Model

The growing concerns regarding the depletion of oil/gas reserves and global warming have made it inevitable to seek energy from wind and other renewable energy resources. Forecasting wind speed is a challenging topic and has important applications in the design and operation of wind power systems, particularly grid connected renewable energy systems, and where forecasting wind speed helps in manipulating the load on the grid. Modern machine learning techniques including neural networks have been widely used for this purpose. As per literature, various models for estimating the hourly wind speed one hour ahead and the hourly wind speed data profile one day ahead have been developed. This paper proposes the use of Artificial Intelligence methods (AI) which are most suitable for the prediction and have provided best results in many situations. AI method involves nonlinear (or linear) and highly complex statistical relationships between input and output data, such as neural networks, fuzzy logic methods, Knearest Neighbors algorithm (KNN) and Support Vector Machine (SVM). AI methods are promising alternatives for predicting wind speed and understanding the wind behavior for a particular region. In the present study (as a case-study), hourly average wind speed data of 13 years (1970-1982) of Qaisumah, Saudi Arabia has been used to evaluate the performance of ANN model. This data has been used for training the neural network. ANN is trained multiple times with different number of hidden neurons to forecast accurate wind speed. The efficiency of proposed model is validated by predicting wind speed of the Qaisumah region with the measured data. Mean Square Error (MSE) and mean absolute percentage error (MAPE values) for proposed model are found to be 0.0912 and 6.65% respectively.

scholarly journals Chaos Time Series Prediction Based on Membrane Optimization Algorithms

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Meng Li ◽  
Liangzhong Yi ◽  
Zheng Pei ◽  
Zhisheng Gao ◽  
Hong Peng
Keyword(s):  
Time Series ◽  
Mean Square Error ◽  
Optimization Algorithm ◽  
Membrane Computing ◽  
Spectrum Management ◽  
Single Step ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Proposed Model

This paper puts forward a prediction model based on membrane computing optimization algorithm for chaos time series; the model optimizes simultaneously the parameters of phase space reconstruction(τ,m)and least squares support vector machine (LS-SVM)(γ,σ)by using membrane computing optimization algorithm. It is an important basis for spectrum management to predict accurately the change trend of parameters in the electromagnetic environment, which can help decision makers to adopt an optimal action. Then, the model presented in this paper is used to forecast band occupancy rate of frequency modulation (FM) broadcasting band and interphone band. To show the applicability and superiority of the proposed model, this paper will compare the forecast model presented in it with conventional similar models. The experimental results show that whether single-step prediction or multistep prediction, the proposed model performs best based on three error measures, namely, normalized mean square error (NMSE), root mean square error (RMSE), and mean absolute percentage error (MAPE).

An Improved Multivariate Weather Prediction Model Using Deep Neural Networks and Particle Swarm Optimisation

2021 ◽  
pp. 2150029
Author(s):  
K U Jaseena ◽  
Binsu C Kovoor
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Weather Prediction ◽  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
Dew Point ◽  
Percentage Error ◽  
Support Vector ◽  
Efficiency Coefficient ◽  
Proposed Model

Accurate weather prediction is always a challenge for meteorologists. This paper suggests a Deep Neural Network (DNN) model to predict minimum and maximum values of temperature based on various weather parameters such as humidity, dew point, and wind speed. Particle Swarm Optimisation (PSO) algorithm is applied to select relevant and important features of the datasets to improve the prediction accuracy of the model. The grid search algorithm is employed to determine the hyperparameters of the proposed DNN model. The statistical indicators Mean Square Error, Mean Absolute Error, Mean Absolute Percentage Error, Nash–Sutcliffe model efficiency coefficient, and Correlation Coefficient are used to evaluate the accuracy of the prediction model. Performance comparison of the proposed model is performed with the Support Vector Machine (SVM) and Vector Autoregression (VAR) models. The experimental outcomes show that the proposed model optimised using PSO achieves better prediction accuracy than traditional approaches.

scholarly journals Deep learning model for daily rainfall prediction: case study of Jimma, Ethiopia

2021 ◽  
Author(s):  
Demeke Endalie ◽  
Getamesay Haile ◽  
Wondmagegn Taye
Keyword(s):  
Agricultural Sector ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Daily Rainfall ◽  
Weather Conditions ◽  
Percentage Error ◽  
Support Vector ◽  
Rainfall Forecasting ◽  
Rainfall Prediction ◽  
Proposed Model

Abstract Rainfall prediction is a critical task because many people rely on it, particularly in the agricultural sector. Rainfall forecasting is difficult due to the ever-changing nature of weather conditions. In this study, we carry out a rainfall predictive model for Jimma, a region located in southwestern Oromia, Ethiopia. We proposed a Long Short-Term Memory (LSTM)-based prediction model capable of forecasting Jimma's daily rainfall. Experiments were conducted to evaluate the proposed models using various metrics such as Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) Nash-Sutcliffe model efficiency (NSE), and R2, and the results were 0.01, 0.4786 0.81 and 0.9972, respectively. We also compared the proposed model to existing machine learning regressions like Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree (DT). The RMSE of MLP was the lowest of the four existing learning models i.e., 0.03. The proposed LSTM model outperforms the existing models, with an RMSE of 0.01. The experimental results show that the proposed model has a lower RMSE and a higher R2.

scholarly journals Transmission potential and forecasting of the number of Coronavirus disease 2019 cases in Hubei Province, China

2020 ◽  
Author(s):  
Ke-wei Wang ◽  
Jie Gao ◽  
Hua Wang ◽  
Xiao-long Wu ◽  
Qin-fang Yuan ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Arima Model ◽  
Hubei Province ◽  
Transmission Dynamics ◽  
Percentage Error ◽  
Terminal Phase ◽  
Support Vector ◽  
Temporal And Spatial Distribution ◽  
Svm Model ◽  
Temporal And Spatial

Abstract Background: Coronavirus disease 2019 (COVID-19) was first reported in Wuhan, Hubei Province, China. We aimed to describe the temporal and spatial distribution and the transmission dynamics of COVID-19 and to assess whether a hybrid model can forecast the trend of COVID-19 in Hubei Province.Method: The data of COVID-19 cases were obtained from the website of the Chinese Center for Disease Control and Prevention, whereas the data on the resident population were obtained from the website of the Hubei Provincial Bureau of Statistics. The temporal and spatial distribution and the transmission dynamics of COVID-19 were described. A combination of an autoregressive integrated moving average (ARIMA) and a support vector machine (SVM) was constructed to forecast the trend of COVID-19.Results: A total of 56,062 confirmed COVID-19 cases, which were mainly concentrated in Wuhan, were reported from 16 January to 16 March 2020 in Hubei Province. The daily number of confirmed cases exponentially increased to 3,156 before 4 February 2020, fluctuated on an upward trend to 4,823 before 13 February 2020, and then markedly decreased to one case after 16 March 2020. The highest mean reproduction number R(t) of 9.48 was recorded on 16 January 2020, after which it decreased to 2.15 on 2 February 2020 and further dropped to less than one on 13 February 2020. In the modelling stage, the mean square error, mean absolute error and mean absolute percentage error of the hybrid ARIMA–SVM model decreased by 98.59%, 89.19% and 89.68%, and those of SVM decreased by 98.58%, 87.71% and 88.94% compared with the ARIMA model. Similar results were obtained in the forecasting stage.Conclusion: Public health interventions resulted in the terminal phase of COVID-19 in Hubei Province. The hybrid ARIMA–SVM model may be a reliable tool for forecasting the trend of the COVID-19 epidemic.

Workload Prediction on Google Cluster Trace

2014 ◽  
Vol 6 (3) ◽  
pp. 34-52 ◽  
Author(s):  
Md. Rasheduzzaman ◽  
Md. Amirul Islam ◽  
Rashedur M. Rahman
Keyword(s):  
Resource Utilization ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Experimental Result ◽  
Percentage Error ◽  
Support Vector ◽  
Workload Prediction ◽  
Inference System ◽  
Squared Error ◽  
Efficient Resource

Workload prediction in cloud systems is an important task to ensure maximum resource utilization. So, a cloud system requires efficient resource allocation to minimize the resource cost while maximizing the profit. One optimal strategy for efficient resource utilization is to timely allocate resources according to the need of applications. The important precondition of this strategy is obtaining future workload information in advance. The main focus of this analysis is to design and compare different forecasting models to predict future workload. This paper develops model through Adaptive Neuro Fuzzy Inference System (ANFIS), Non-linear Autoregressive Network with Exogenous inputs (NARX), Autoregressive Integrated Moving Average (ARIMA), and Support Vector Regression (SVR). Public trace data (workload trace version II) which is made available by Google were used to verify the accuracy, stability and adaptability of different models. Finally, this paper compares these prediction models to find out the model which ensures better prediction. Performance of forecasting techniques is measured by some popular statistical metric, i.e., Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Sum of Squared Error (SSE), Normalized Mean Squared Error (NMSE). The experimental result indicates that NARX model outperforms other models, e.g., ANFIS, ARIMA, and SVR.

scholarly journals A Hybrid Arima and Discrete Wavelet Transform Model for Predicting the Electricity Consumption of Punjab

2019 ◽  
Vol 8 (11) ◽  
pp. 1915-1919
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Arima Model ◽  
Electricity Consumption ◽  
Electricity Production ◽  
Series Data ◽  
Support Vector ◽  
Discrete Wavelet ◽  
World Population ◽  
Proposed Model

The significant increase in the world population increases the demand for energy which seems to be alarming for the electricity production boards in the existing time. In the last decade, there are various engineering, simulation tools, and artificial intelligence-based methods such as Support Vector Machine and Artificial Neural Network proposed in the literature to forecast the optimal electricity demand. But these models seldom to work with the linear data. In this paper, a reliable prediction model using the linear time series data of the previous years from January 2013 to December 2017 has been presented to forecast the electricity consumption in Punjab, India. Initially, Discrete Wavelet Transform (DWT) analysis presented to extract the upper and lower limit of the previous year dataand then AutoRegressive Integrated Moving Average (ARIMA) model has been applied to extract the forecast values. The experimental results compared the original and predicted value using the proposed model to evaluate the effectiveness of the proposed approach. The results show that the difference between the original and proposed modelis only 9% while that of ARIMA only it is 11%. Thus, the proposed model using ARIMA and DWT provides effective results in predicting the forecast value.

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