Regression and ARIMA hybrid model for new bug prediction
نویسندگان
چکیده
A multiple linear regression and ARIMA hybrid model is proposed for new bug prediction depending upon resolved bugs and other available parameters of the open source software bug report. Analysis of last five year bug report data of a open source software “worldcontrol” is done to identify the trends followed by various parameters. Bug report data has been categorized on monthly basis and forecast is also on monthly basis. Model accounts for the parameters such as resolved, assigned, reopened, closed and verified bugs respectively. Real time monthly data of these parameters from 2003 to 2007 is taken for multiple regression then hybrid model does monthly forecast for 2008. Model is basically hybrid of linear regression and ARIMA(p,0,p) where p = 1,2,3. Results show that monthly forecast of new bugs considering five predefined factors is far more accurate by hybrid model than just time series ARIMA forecast of new bugs. Hybrid of linear regression and ARIMA (3,0,3) gave best results. Keywords-regression;hybrid;ARIMA
منابع مشابه
Forecasting Air Pollution Concentrations in Iran, Using a Hybrid Model
The present study aims at developing a forecasting model to predict the next year’s air pollution concentrations in the atmosphere of Iran. In this regard, it proposes the use of ARIMA, SVR, and TSVR, as well as hybrid ARIMA-SVR and ARIMA-TSVR models, which combined the autoregressive part of the autoregressive integrated moving average (ARIMA) model with the support vector regression technique...
متن کاملForecasting Air Pollution Concentrations in Iran, Using a Hybrid Model
The present study aims at developing a forecasting model to predict the next year’s air pollution concentrations in the atmosphere of Iran. In this regard, it proposes the use of ARIMA, SVR, and TSVR, as well as hybrid ARIMA-SVR and ARIMA-TSVR models, which combined the autoregressive part of the autoregressive integrated moving average (ARIMA) model with the support vector regression technique...
متن کاملForecasting Stock Price using Hybrid Model based on Wavelet Transform in Tehran and New York Stock Market
Forecasting financial markets is an important issue in finance area and research studies. On one hand, the importance of prediction, and on the other hand, its complexity, have led to huge number of researches which have proposed many forecasting methods in this area. In this study, we propose a hybrid model including Wavelet Transform, ARMA-GARCH and Artificial Neural Network (ANN) for single-...
متن کاملPerformance Evaluation of ARIMA Hybrid Models in the Prediction of Daily Electrical Conductivity (A Case Study of Telazang Hydrometric Station)
In this study, we used the ARIMA time series model, the fuzzy-neural inference network, multi-layer perceptron artificial neural network, and ARIMA-ANN, ARIMA-ANFIS hybrid models for the modeling and prediction of the daily electrical conductivity parameter of daily teleZang hydrometric station over the statistical period of 49 years. For this purpose, the daily data for the 1996-2004 period we...
متن کاملAn Improved Hybrid Model with Automated Lag Selection to Forecast Stock Market
Objective: In general, financial time series such as stock indexes have nonlinear, mutable and noisy behavior. Structural and statistical models and machine learning-based models are often unable to accurately predict series with such a behavior. Accordingly, the aim of the present study is to present a new hybrid model using the advantages of the GMDH method and Non-dominated Sorting Genetic A...
متن کامل