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An Intelligent Forecasting Model for Disease Prediction Using Stack Ensembling Approach

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Computers, Materials & Continua 2022年第3期70卷 6041-6055页

作者： Shobhit Verma Nonita Sharma Aman Singh Abdullah Alharbi Wael Alosaimi Hashem Alyami Deepali Gupta Nitin Goyal Computer Science&Engineering Department Dr.B.R.Ambedkar National Institute of TechnologyJalandharIndia Computer Science&Engineering Department Lovely Professional UniversityJalandharIndia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia

This research work proposes a new stack-based generalization ensemble model to forecast the number of incidences of conjunctivitis *** addition to forecasting the occurrences of conjunctivitis incidences,the proposed model also improves performance by using the ensemble *** rate of acute Conjunctivitis per 1000 for Hong Kong is collected for the duration of the first week of January 2010 to the last week of December ***-processing techniques such as imputation of missing values and logarithmic transformation are applied to pre-process the data sets.A stacked generalization ensemble model based on Auto-ARIMA(Autoregressive Integrated Moving Average),NNAR(Neural Network Autoregression),ETS(exponential smoothing),HW(Holt Winter)is proposed and applied on the *** analysis is conducted on the collected dataset of conjunctivitis disease,and further compared for different performance *** result shows that the RMSE(Root Mean Square Error),MAE(Mean Absolute Error),MAPE(Mean Absolute Percentage Error),ACF1(Auto Correlation Function)of the proposed ensemble is decreased *** the RMSE,for instance,error values are reduced by 39.23%,9.13%,20.42%,and 17.13%in comparison to Auto-ARIMA,NAR,ETS,and HW model *** research concludes that the accuracy of the forecasting of diseases can be significantly increased by applying the proposed stack generalization ensemble model as it minimizes the prediction error and hence provides better prediction trends as compared to Auto-ARIMA,NAR,ETS,and HW model applied discretely.

关键词： Disease prediction stack ensemble neural network autoregression exponential smoothing auto-ARIMA holt winter

Augmented Winter’s method for forecasting under asynchronous seasonalities

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Journal of Management Analytics 2021年第1期8卷 19-35页

作者： Oktay Karabag MMurat Fadıloglu School of Economics Erasmus University RotterdamRotterdamThe Netherlands Department of Industrial Engineering American University of the Middle EastEgailaKuwait

The method of Winters(1960)is one of the most well-known forecasting methodologies in *** main reason behind its popularity is that it is easy to implement and can give quite effective and efficient results for practice ***,this method is not capable of capturing a pattern being emerged due to the simultaneous effects of two different asynchronous calendars,such as Gregorian and *** adapt this method in a way that it can deal with such patterns,and study its performance using a real dataset collected from a brewery factory in *** the same data set,we also provide a comparative performance analysis between our model and several forecasting models such as Winter’s(Winters 1960),TBAT(De Livera et al.2011),ETS(Hyndman et al.2002),and ARIMA(Hyndman and Khandakar 2008).The results we obtained reveal that better forecasts can be achieved using the new method when two asynchronous calendars exert their effects on the time-series.

关键词： sales forecasting Ramadan effect asynchronous calendar effects exponential smoothing

Comparing practice-ready forecast models for weekly and monthly fluctuations of average daily traffic and enhancing accuracy by weighting methods

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Journal of Traffic and Transportation Engineering(English Edition) 2018年第4期5卷 239-253页

作者： Andrea Pompigna Federico Rupi Department of Civil Chemical Environmental and Materials Engineering University of Bologna Bologna 40136 Italy R&M Associati Bologna 40134 Italy

Knowing daily traffic for the current year is recognized as being essential in many fields of transport analysis and practice, and short-term forecasting models offer a set of tools to meet these needs. This paper examines and compares the accuracy of three representative parametric and non-parametric prediction models, selected by the analysis of the numerous methods proposed in the literature for their good combi- nation of forecast accuracy and ease of calibration, using real-life data on Italian motorway stretches. Non-parametric K-NN regression model, Gaussian maximum likelihood model and double seasonality Holt-Winters exponential smoothing model confirm their goodness to predict the weekly and monthly fluctuations of average daily traffic with varying degrees of performance, while maintaining an easy use in professional practice, i.e. requiring ordinary professional skills and conventional analysis tools. Since combining several prediction models can give, on average, more accuracy than that of the individual models, the paper compares two weighting methods of easy implementation and susceptible to a direct use, namely the widely used information entropy method and the less widespread Shapley value method. Despite being less common than the information entropy method, the Shapley value method proves to be more capable in better combining single forecasts and produces improvements in the predictions for test data. With these remarks, the paper might be of interest to traffic technicians or analysts, in various and not uncommon tasks they might find in their work.

关键词： Short-term traffic forecasting Non-parametric regression Gaussian maximum likelihood Double seasonal Holt-Winters exponential smoothing Entropy weighting method Shapley value weighting method

Estimation of demand for wood panels in Iran by the year of 2012.

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Journal of Forestry Research 2009年第A2期20卷 179-182,I0004页

作者： Majid Azizi Payam Ghorbannezhad Hamid Hatefnia Department of wood and paper sciences and technology College of natural resources University of Tehran P.O. Box： 31585-4314 Karaj lran

Considering increasing rate of Iran population and consumption of wood panels, the authors investigated the per capita consump- tion of wood panels during the years from 1997 to 2007. The exponential smoothing method was used to obtain a per capita consumption panern of wood panels in lran for estimating demand of wood panels by the year of 2012. Results show that the consumption of particleboard, fiberboard, and medium density fiberboard in lran will increase by 33%, 72% and 107 %o, respectively, by the year of 2012; however, the consumption of plywood will increase only by 7% by 2012. The deficient amount of wood panels in Iran is estimated over 1400 000 m^3. The results of this study provide the technique reference for planners of wood panel industries in Iran in capital investment decisions.

关键词： wood panels exponential smoothing estimating Iran wood panel demand

Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale

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Geoscience Frontiers 2022年第3期13卷 79-99页

作者： Georgia Papacharalampous Hristos Tyralis Ilias G.Pechlivanidis Salvatore Grimaldi Elena Volpi Department of Engineering Roma Tre UniversityVia V.Volterra 6200146 RomeItaly Department of Water Resources and Environmental Engineering School of Civil EngineeringNational Technical University of AthensHeroon Polytechneiou 515780 ZographouGreece Hellenic Air Force General Staff Hellenic Air ForceMesogion Avenue 227-23115561 CholargosGreece Swedish Meteorological and Hydrological Institute 60176 NorrköpingSweden Department for Innovation in Biological Agro-food and Forest SystemsUniversity of TusciaViterboItaly Department of Mechanical and Aerospace Engineering Tandon School of EngineeringNew York UniversityBrooklynNY10003United States

Statistical analyses and descriptive characterizations are sometimes assumed to be offering information on time series *** the scientific interest suggested by such assumptions,the relationships between descriptive time series features(e.g.,temporal dependence,entropy,seasonality,trend and linearity features)and actual time series forecastability(quantified by issuing and assessing forecasts for the past)are scarcely studied and quantified in the *** this work,we aim to fill in this gap by investigating such relationships,and the way that they can be exploited for understanding hydroclimatic forecastability and its *** this end,we follow a systematic framework bringing together a variety of–mostly new for hydrology–concepts and methods,including 57 descriptive features and nine seasonal time series forecasting methods(i.e.,one simple,five exponential smoothing,two state space and one automated autoregressive fractionally integrated moving average methods).We apply this framework to three global datasets originating from the larger Global Historical Climatology Network(GHCN)and Global Streamflow Indices and Metadata(GSIM)*** these datasets comprise over 13,000 monthly temperature,precipitation and river flow time series from several continents and hydroclimatic regimes,they allow us to provide trustable characterizations and interpretations of 12-month ahead hydroclimatic forecastability at the global *** first find that the exponential smoothing and state space methods for time series forecasting are rather equally efficient in identifying an upper limit of this forecastability in terms of Nash-Sutcliffe efficiency,while the simple method is shown to be mostly useful in identifying its lower *** then demonstrate that the assessed forecastability is strongly related to several descriptive features,including seasonality,entropy,(partial)autocorrelation,stability,(non)linearity,spikiness and heterogeneity features

关键词： exponential smoothing Predictability Statistical hydrology Time series analysis Time series clustering Time series forecasting

Oil-Price Forecasting Based on Various Univariate Time-Series Models

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American Journal of Operations Research 2016年第3期6卷 226-235页

作者： Gurudeo Anand Tularam Tareq Saeed Mathematics and Statistics Griffith Sciences (ENV) Environmental Futures Research Institute (EFRI) Griffith University Brisbane Australia Department of Mathematics Faculty of Science King Abdulaziz University Jeddah KSA

Time-series-based forecasting is essential to determine how past events affect future events. This paper compares the performance accuracy of different time-series models for oil prices. Three types of univariate models are discussed: the exponential smoothing (ES), Holt-Winters (HW) and autoregressive intergrade moving average (ARIMA) models. To determine the best model, six different strategies were applied as selection criteria to quantify these models’ prediction accuracies. This comparison should help policy makers and industry marketing strategists select the best forecasting method in oil market. The three models were compared by applying them to the time series of regular oil prices for West Texas Intermediate (WTI) crude. The comparison indicated that the HW model performed better than the ES model for a prediction with a confidence interval of 95%. However, the ARIMA (2, 1, 2) model yielded the best results, leading us to conclude that this sophisticated and robust model outperformed other simple yet flexible models in oil market.

关键词： Oil Price Univariate Time Series exponential smoothing Holt-Winters ARIMA Models Model Selection Criteria

Forecasting Spare Parts Demand Using Statistical Analysis

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American Journal of Operations Research 2016年第2期6卷 113-120页

作者： Raghad Hemeimat Lina Al-Qatawneh Mazen Arafeh Shadi Masoud Department of Industrial Engineering University of Jordan Amman Jordan

Spare parts are very essential in most industrial companies. They are characterized by their large number and their high impact on the companies’ operations whenever needed. Therefore companies tend to analyze their spare parts demand and try to estimate their future consumption. Nevertheless, they face difficulties in figuring out an optimal forecasting method that deals with the lumpy and intermittent demand of spare parts. In this paper, we performed a comparison between five forecasting methods based on three statistical tools;Mean squared error (MSE), mean absolute deviation (MAD) and mean error (ME), where the results showed close performance for all the methods associated with their optimal parameters and the frequency of the spare part demand. Therefore, we proposed to compare all the methods based on the tracking signal with the objective of minimizing the average number of out of controls. This approach was tested in a comparative study at a local paper mill company. Our findings showed that the application of the tracking signal approach helps companies to better select the optimal forecasting method and reduce forecast errors.

关键词： Forecasting Spare Parts FSN Moving Average exponential smoothing Tracking Signal

Empirical Modeling of Annual Fishery Landings

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Natural Resources 2016年第4期7卷 193-204页

作者： Eric Adjei Lawer Department of Range and Wildlife Management Faculty of Renewable Natural Resources University for Development Studies Nyankpala Campus Tamale Ghana

Forecasting plays an essential role in policy formulation and implementation especially in the management of fisheries resources. In this paper, various techniques of forecasting using time series analysis were evaluated on annual fishery production data. In addition to the Box-Jenkins approach, other methods such as the feed forward neural network and exponential smoothing approaches were also examined. A parsimonious model for each forecasting approach was then selected using penalized likelihoods. The chosen models were then evaluated based on their ability to produce accurate forecasts. Implications of the findings as discussed revealed that no particular method was ideal for modeling all landings. Hence when forecasting fishery landings, it is recommended that different structural approaches be compared before selecting an appropriate one for use.

关键词： Artificial Neural Network ARIMA exponential smoothing Forecasting Fish Ghana

Trust-Based Service Matching Access Control Model

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Trust-Based Service Matching Access Control Model