Fitting ARIMA model for volatility insurance time series data

The volatility of stock  market data have contributed an essential section in risk study and it is very serious problem especially in emerging markets. Previously it is measured by standard deviation of the return. Therefore, in this article the volatility data will be predicted based on Autoregressive Integrated Moving Average model  (ARIMA) using insurance stock market data from Amman Stock Exchange (ASE) from January 2019 to December 2019. As a result this article shows that the ARIMA model has significant results for short-term prediction. Therefore, These results will be helpful for the investments. Keywords: ARIMA model, forecasting, Insurance Sector DOI: 10.7176/EJBM/11-36-13 Publication date: December 31st 201

Analisis Kinerja Gabungan Metode Ensemble Empirical Mode Decomposition Dan Generalized Regression Neural Network

The method of time series suitable for use when it checks each data patterns systematically and has many variables, such as in the case of crude oil prices. One study that utilizes the methods of time series is the integration between Ensemble Empirical Mode Decomposition (EEMD) and neural network algorithms based on Polak-Ribiere Conjugate Gradient (PCG). However, PCG requires setting free parameters in the learning process. Meanwhile, the appropriate parameters are needed to get accurate forecasting results. This research proposes the integration between EEMD and Generalized Regression Neural Network (GRNN). GRNN has advantages, such as: does not require any parameter settings and a quick learning process. For the evaluation, the performance of the method EEMD-GRNN compared with GRNN. The experimental results showed that the method EEMD-GRNN produce better forecasting of GRNN.Metode runtun waktu cocok digunakan ketika akan memeriksa setiap pola data secara sistematis dan memiliki banyak variabel bebas, seperti pada kasus harga minyak mentah. Salah satu penelitian yang memanfaatkan metode runtun waktu adalah integrasi antara Ensemble Empirical Mode Decomposition (EEMD) dan jaringan syaraf berdasarkan algoritma Polak-Ribiére Conjugate Gradient (PCG). Namun, PCG memerlukan pengaturan parameter bebas dalam proses pembelajarannya. Sementara, parameter yang sesuai sangat dibutuhkan  untuk mendapatkan hasil peramalan yang akurat. Penelitian ini mengusulkan integrasi antara EEMD dan Generalized Regression Neural Network (GRNN). GRNN memiliki keunggulan, yaitu: tidak memerlukan pengaturan parameter dan proses pembelajaran yang cepat. Untuk evaluasi, kinerja metode EEMD-GRNN dibandingkan  dengan GRNN. Hasil eksperimen menunjukkan bahwa metode EEMD-GRNN menghasilkan peramalan yang lebih baik dari GRNN

A New Hybrid Methodology for Nonlinear Time Series Forecasting

Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of forecasting problems with a high degree of accuracy. However, using ANNs to model linear problems have yielded mixed results, and hence; it is not wise to apply them blindly to any type of data. This is the reason that hybrid methodologies combining linear models such as ARIMA and nonlinear models such as ANNs have been proposed in the literature of time series forecasting. Despite of all advantages of the traditional methodologies for combining ARIMA and ANNs, they have some assumptions that will degenerate their performance if the opposite situation occurs. In this paper, a new methodology is proposed in order to combine the ANNs with ARIMA in order to overcome the limitations of traditional hybrid methodologies and yield more general and more accurate hybrid models. Empirical results with Canadian Lynx data set indicate that the proposed methodology can be a more effective way in order to combine linear and nonlinear models together than traditional hybrid methodologies. Therefore, it can be applied as an appropriate alternative methodology for hybridization in time series forecasting field, especially when higher forecasting accuracy is needed

Application of ARIMA and Artificial Neural Networks Models for Daily Cumulative Confirmed Covid-19 Prediction in Nigeria

Coronavirus 2019, commonly referred to as COVID-19, is a disease discovered in China towards the end of December 2019. This novel and highly infectious virus spreads rapidly across the globe. In Nigeria, as of June 2020, the cumulative number of COVID-19 cases reported was 25,694: out of this, 9746 cases were treated and 590 cases lost their lives. This research was aimed at comparing the prediction ability of ARIMA and ANN models. The aggregate COVID-19 cases reported in Nigeria was subjected to Box-Jenkins time series and  Back propagation gradient-based Artificial Neural Network Approaches for the prediction purpose. The data obtained from the Nigeria Centre for Disease Control (NCDC) was used. The data were identified to follow ARIMA (1, 2, 1) and were best trained by Bayesian  Regularization Artificial Neural Network algorithm. The prediction performance of the two models were compared using RMSE, MAE and MAPE. The empirical results obtained show that the Artificial Neural Network model gives better predictions and forecasts over the ARIMAmodel

Fuzzy-neural model with hybrid market indicators for stock forecasting

A number of research had been carried out to forecast stock price based on technical indicators, which rely purely on historical stock price data. Nevertheless, their performance is not always satisfactory. In this paper, the effect of using hybrid market indicators of technical, fundamental indicators and experts opinion for stock price prediction is examined. Input variables extracted from these market hybrid indicators are fed into a fuzzy-neural network for improved accuracy of stock price prediction. The empirical results obtained with published stock data shows that the proposed model can be effective to improve accuracy of stock price prediction

An Improved Stock Price Prediction using Hybrid Market Indicators

In this paper the effect of hybrid market indicators is examined for an improved stock price prediction. The hybrid market indicators consist of technical, fundamental and expert opinion variables as input to artificial neural networks model. The empirical results obtained with published stock data of Dell and Nokia obtained from New York Stock Exchange shows that the proposed model can be effective to improve accuracy of stock price prediction

Enhance interval width of crime forecasting with ARIMA model-fuzzy alpha cut

With qualified data or information a better decision can be made. The interval width of forecasting is one of data values to assist in the selection decision making process in regards to crime prevention. However, in time series forecasting, especially the use of ARIMA model, the amount of historical data available can affect forecasting result including interval width forecasting value. This study proposes a combination technique, in order to get get a better interval width crime forecasting value. The propose combination technique between ARIMA model and Fuzzy Alpha Cut are presented. The use of variation alpha values are used, they are 0.3, 0.5, and 0.7. The experimental results have shown the use of ARIMA-FAC with alpha=0.5 is appropriate. The overall results obtained have shown the interval width crime forecasting with ARIMA-FAC is better than interval width crime forecasting with 95% CI ARIMA model

Greenhouse Gas Emissions: Historical and Projected Methane Emissions from Rice Cultivation in Malaysia (1990-2030)

Global warming and climate change has reached the alarming levels due to increase of greenhouse gas emissions into the atmosphere which includes carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O). Flooded rice (Oryza sativa L.) cultivation has been identified as one of the prominent global agricultural sources of anthropogenic CH4 emissions. Moreover, it has been estimated that global rice production is responsible for 11% of total anthropogenic CH4 emissions. The inventory of CH4 emission from rice cultivation in Malaysia was estimated from 1990 to 2014 and was also used as basis for computing the projected emissions up to 2030 by using Auto-Regressive Integrated Moving Average (ARIMA) model. Results showed that CH4 emissions is higher from granary area (continuously flooded) than non-granary area (rain-fed) due to different water management practices. Continuously flooded irrigation system which lead to anaerobic conditions emit almost (75%) higher CH4 than rain-fed irrigation system. Emissions forecasted will be continuously increase from 2015 to 2030 within the confidence limits. Emissions were forecasted to increase up to 88 Gg by 2030 due to increase of country population which will lead to expansion of cultivation area in order to fulfil country needs

An Improved Model for Stock Price Prediction using Market Experts Opinion

Several research efforts had been done to forecast stock price based on technical indicators which rely purely on historical stock price data. Nevertheless, their performance is not always satisfactory. However, there are other influential factors which can affect the direction of stock market which form the basis of market experts’ opinion such as interest rate, inflation rate, foreign exchange rate, business sector, management caliber, government policy and political effects among others. In this paper, the effect of using market experts’ opinion in addition to the use of technical and fundamental indicators for stock price prediction is examined. Input variables extracted from these hybrid indicators are fed into a fuzzy-neural network for improved accuracy of stock price prediction. The empirical results obtained with published stock data shows that the proposed model can be effective to improving accuracy of stock price predictio