Relevant bibliographies by topics / Forecasting ; Predictive Analytics

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Forecasting ; Predictive Analytics'

Author: Grafiati
Published: 4 June 2021
Last updated: 17 February 2022

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Journal articles on the topic "Forecasting ; Predictive Analytics"

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Paukova, Yulia V., and Konstantin V. Popov. "FORECASTING MIGRATION FLOWS USING PREDICTIVE ANALYTICS." SCIENTIFIC REVIEW. SERIES 1. ECONOMICS AND LAW, no. 1-2 (2020): 45–54. http://dx.doi.org/10.26653/2076-4650-2020-1-2-04.

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The present article considers the need to predict migration flows using Predictive Analytics. The Russian Federation is a center of migration activity. The modern world is changing rapidly. An effective migration policy requires effective monitoring of migration flows, assessing the current situation in our and other countries and forecasting migration processes. There are information systems in Russia that contain a wide range of information about foreign citizens and stateless persons that provide the requested information about specific foreign citizens, including grouping it on various grounds. However, it is not possible to analyze and predict it automatically using thousands of parameters. Special attention in Russia is paid to digitalization. Using information technologies (artificial intelligence, machine learning and big data analysis) to forecast migration flows in conditions of variability of future events will allow to take into account a number of events and most accurately predict the quantitative and so-called "qualitative" structure of arrivals. The received information will help to develop state policy and to take appropriate measures in the field of migration regulation. The authors come to the conclusion that it is necessary to amend existing legal acts in order to implement information technologies of Predictive Analytics into the practice of migration authorities.
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Holthoff, Gero, and René Decher. "Implementierung von Predictive Analytics im Forecasting." Controlling 32, no. 6 (2020): 53–59. http://dx.doi.org/10.15358/0935-0381-2020-6-53.

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Der Einsatz von Predictive Analytics im Forecasting hat sich trotz seiner Vorteile noch nicht umfänglich in der Praxis etabliert. Dies ist u. a. verschiedenen Herausforderungen bei der Implementierung geschuldet, wie z. B. der menschlichen Abneigung gegenüber algorithmusbasierten Prognosen (Algorithm Aversion). Diese Herausforderungen werden näher beleuchtet und Lösungsmöglichkeiten aufgezeigt.
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Maciejewski, R., R. Hafen, S. Rudolph, et al. "Forecasting Hotspots—A Predictive Analytics Approach." IEEE Transactions on Visualization and Computer Graphics 17, no. 4 (2011): 440–53. http://dx.doi.org/10.1109/tvcg.2010.82.

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Fitzpatrick, Dylan J., Wilpen L. Gorr, and Daniel B. Neill. "Keeping Score: Predictive Analytics in Policing." Annual Review of Criminology 2, no. 1 (2019): 473–91. http://dx.doi.org/10.1146/annurev-criminol-011518-024534.

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Predictive analytics in policing is a data-driven approach to ( a) characterizing crime patterns across time and space and ( b) leveraging this knowledge for the prevention of crime and disorder. This article outlines the current state of the field, providing a review of forecasting tools that have been successfully applied by police to the task of crime prediction. We then discuss options for structured design and evaluation of a predictive policing program so that the benefits of proactive intervention efforts are maximized given fixed resource constraints. We highlight examples of predictive policing programs that have been implemented and evaluated by police agencies in the field. Finally, we discuss ethical issues related to predictive analytics in policing and suggest approaches for minimizing potential harm to vulnerable communities while providing an equitable distribution of the benefits of crime prevention across populations within police jurisdiction.
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Furmanchuk, Al'ona, Ankit Agrawal, and Alok Choudhary. "Predictive analytics for crystalline materials: bulk modulus." RSC Advances 6, no. 97 (2016): 95246–51. http://dx.doi.org/10.1039/c6ra19284j.

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The machine learning-based generalized model developed for forecasting bulk moduli of various types of stoichiometric and non-stoichiometric crystalline materials. The web application (ThermoEl) deploying the developed predictive model is available for public use.
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Sharma, Aastha, and Vijayakumar V. "Predictive Analytics In Weather Forecasting Using Machine Learning Algorithms." EAI Endorsed Transactions on Cloud Systems 5, no. 14 (2019): 159405. http://dx.doi.org/10.4108/eai.7-12-2018.159405.

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Iftikhar, Rehan, and Mohammad Saud Khan. "Social Media Big Data Analytics for Demand Forecasting." Journal of Global Information Management 28, no. 1 (2020): 103–20. http://dx.doi.org/10.4018/jgim.2020010106.

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Social media big data offers insights that can be used to make predictions of products' future demand and add value to the supply chain performance. The paper presents a framework for improvement of demand forecasting in a supply chain using social media data from Twitter and Facebook. The proposed framework uses sentiment, trend, and word analysis results from social media big data in an extended Bass emotion model along with predictive modelling on historical sales data to predict product demand. The forecasting framework is validated through a case study in a retail supply chain. It is concluded that the proposed framework for forecasting has a positive effect on improving accuracy of demand forecasting in a supply chain.
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Reddy, Pundru, and Alladi Sureshbabu. "An Adaptive Model for Forecasting Seasonal Rainfall Using Predictive Analytics." International Journal of Intelligent Engineering and Systems 12, no. 5 (2019): 22–32. http://dx.doi.org/10.22266/ijies2019.1031.03.

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Dutt, Raman, and Vinita Krishna. "Forecasting the Grant Duration of a Patent using Predictive Analytics." International Journal of Computer Applications 178, no. 51 (2019): 1–7. http://dx.doi.org/10.5120/ijca2019919398.

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Sabu, Kiran M., and T. K. Manoj Kumar. "Predictive analytics in Agriculture: Forecasting prices of Arecanuts in Kerala." Procedia Computer Science 171 (2020): 699–708. http://dx.doi.org/10.1016/j.procs.2020.04.076.

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Dissertations / Theses on the topic "Forecasting ; Predictive Analytics"

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Alroomi, Azzam J. M. A. H. "Essays in forecasting financial markets with predictive analytics techniques." Thesis, Bangor University, 2018. https://research.bangor.ac.uk/portal/en/theses/essays-in-forecasting-financial-markets-with-predictive-analytics-techniques(a1bc7d33-04b0-416d-82ea-fbefd19da7ff).html.

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This PhD dissertation comprises four essays on forecasting financial markets with unsupervised predictive analytics techniques, most notably time series extrapolation methods and artificial neural networks. Key objectives of the research were reproducibility and replicability, which are fundamental principles in management science and, as such, the implementation of all of the suggested algorithms has been fully automated and completely unsupervised in R. As with any predictive analytics exercise, computational intensiveness is a significant challenge and criterion of performance and, thus, both forecasting accuracy and uncertainty as well as computational times are reported in all essays. Multiple horizons, multiple methods and benchmarks and multiple metrics are employed as dictated by good practice in empirical forecasting exercises. The essays evolve in nature as each one is based on the previous one, testing one more condition as the essays progress, outlined in sequence as follows: which method wins overall in a very extensive evaluation over five frequencies (yearly, quarterly, monthly, weekly and daily data) over 18 time series of stocks with the biggest capitalization from the FTSE 100, over the last 20 years (first essay); the impact of horizon in this exercise and how this promotes different winners for different horizons (second essay); the impact of using uncertainty in the form of maximum-minimum values per period, despite still being interested in forecasting the mean expected value over the next period; and introducing a second variable capturing all other aspects of the behavioural nature of the financial environment – the trading volume – and evaluating whether this improves forecasting performance or not. The whole endeavour required the use of the High Performance Computing Wales (HPC Wales) for a significant amount of time, incurring computational costs that ultimately paid off in terms of increased forecasting accuracy for the AI approaches; the whole exercise for one series can be repeated on a fast laptop device (i7 with 16 GB of memory). Overall (forecasting) horses for (data) courses were once again proved to perform best, and the fact that one method cannot win under all conditions was once more evidenced. The introduction of uncertainty (in terms of range for every period), as well as volume as a second variable capturing environmental aspects, was beneficial with regard to forecasting accuracy and, overall, the research provided empirical evidence that predictive analytics approaches have a future in such a forecasting context. Given this was a predictive analytics exercise, focus was placed on forecasting levels (monetary values) and not log-returns; and out-of-sample forecasting accuracy, rather than causality, was a primary objective, thus multiple regression models were not considered as benchmarks. As in any empirical predicting analytics exercise, more time series, more artificial intelligence methods, more metrics and more data can be employed so as to allow for full generalization of the results, as long as all of these can be fully automated and forecast unsupervised in a freeware environment – in this thesis that being R.
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Naidoo, Jefrey Subramoney. "Forecasting recessions: The convergence of information and predictive analytics." THE UNIVERSITY OF ALABAMA, 2011. http://pqdtopen.proquest.com/#viewpdf?dispub=3439830.

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Aronsson, Henrik. "Modeling strategies using predictive analytics : Forecasting future sales and churn management." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-167130.

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This project was carried out for a company named Attollo, a consulting firm specialized in Business Intelligence and Corporate Performance Management. The project aims to explore a new area for Attollo, predictive analytics, which is then applied to Klarna, a client of Attollo. Attollo has a partnership with IBM, which sells services for predictive analytics. The tool that this project is carried out with, is a software from IBM: SPSS Modeler. Five different examples are given of what and how the predictive work that was carried out at Klarna consisted of. From these examples, the different predictive models' functionality are described. The result of this project demonstrates, by using predictive analytics, how predictive models can be created. The conclusion is that predictive analytics enables companies to understand their customers better and hence make better decisions.<br>Detta projekt har utforts tillsammans med ett foretag som heter Attollo, en konsultfirma som ar specialiserade inom Business Intelligence &amp; Coporate Performance Management. Projektet grundar sig pa att Attollo ville utforska ett nytt omrade, prediktiv analys, som sedan applicerades pa Klarna, en kund till Attollo. Attollo har ett partnerskap med IBM, som saljer tjanster for prediktiv analys. Verktyget som detta projekt utforts med, ar en mjukvara fran IBM: SPSS Modeler. Fem olika exempel beskriver det prediktiva arbetet som utfordes vid Klarna. Fran dessa exempel beskrivs ocksa de olika prediktiva modellernas funktionalitet. Resultatet av detta projekt visar hur man genom prediktiv analys kan skapa prediktiva modeller. Slutsatsen ar att prediktiv analys ger foretag storre mojlighet att forsta sina kunder och darav kunna gora battre beslut.
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Deodhar, Suruchi. "Data Integration Methodologies and Services for Evaluation and Forecasting of Epidemics." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/71303.

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Most epidemiological systems described in the literature are built for evaluation and analysis of specific diseases, such as Influenza-like-illness. The modeling environments that support these systems are implemented for specific diseases and epidemiological models. Hence they are not reusable or extendable. This thesis focuses on the design and development of an integrated analytical environment with flexible data integration methodologies and multi-level web services for evaluation and forecasting of various epidemics in different regions of the world. The environment supports analysis of epidemics based on any combination of disease, surveillance sources, epidemiological models, geographic regions and demographic factors. The environment also supports evaluation and forecasting of epidemics when various policy-level and behavioral interventions are applied, that may inhibit the spread of an epidemic. First, we describe data integration methodologies and schema design, for flexible experiment design, storage and query retrieval mechanisms related to large scale epidemic data. We describe novel techniques for data transformation, optimization, pre-computation and automation that enable flexibility, extendibility and efficiency required in different categories of query processing. Second, we describe the design and engineering of adaptable middleware platforms based on service-oriented paradigms for interactive workflow, communication, and decoupled integration. This supports large-scale multi-user applications with provision for online analysis of interventions as well as analytical processing of forecast computations. Using a service-oriented architecture, we have provided a platform-as-a-service representation for evaluation and forecasting of epidemics. We demonstrate the applicability of our integrated environment through development of the applications, DISIMS and EpiCaster. DISIMS is an interactive web-based system for evaluating the effects of dynamic intervention strategies on epidemic propagation. EpiCaster is a situation assessment and forecasting tool for projecting the state of evolving epidemics such as flu and Ebola in different regions of the world. We discuss how our platform uses existing technologies to solve a novel problem in epidemiology, and provides a unique solution on which different applications can be built for analyzing epidemic containment strategies.<br>Ph. D.
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Fischer, Ulrike. "Forecasting in Database Systems." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-133281.

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Time series forecasting is a fundamental prerequisite for decision-making processes and crucial in a number of domains such as production planning and energy load balancing. In the past, forecasting was often performed by statistical experts in dedicated software environments outside of current database systems. However, forecasts are increasingly required by non-expert users or have to be computed fully automatically without any human intervention. Furthermore, we can observe an ever increasing data volume and the need for accurate and timely forecasts over large multi-dimensional data sets. As most data subject to analysis is stored in database management systems, a rising trend addresses the integration of forecasting inside a DBMS. Yet, many existing approaches follow a black-box style and try to keep changes to the database system as minimal as possible. While such approaches are more general and easier to realize, they miss significant opportunities for improved performance and usability. In this thesis, we introduce a novel approach that seamlessly integrates time series forecasting into a traditional database management system. In contrast to flash-back queries that allow a view on the data in the past, we have developed a Flash-Forward Database System (F2DB) that provides a view on the data in the future. It supports a new query type - a forecast query - that enables forecasting of time series data and is automatically and transparently processed by the core engine of an existing DBMS. We discuss necessary extensions to the parser, optimizer, and executor of a traditional DBMS. We furthermore introduce various optimization techniques for three different types of forecast queries: ad-hoc queries, recurring queries, and continuous queries. First, we ease the expensive model creation step of ad-hoc forecast queries by reducing the amount of processed data with traditional sampling techniques. Second, we decrease the runtime of recurring forecast queries by materializing models in a specialized index structure. However, a large number of time series as well as high model creation and maintenance costs require a careful selection of such models. Therefore, we propose a model configuration advisor that determines a set of forecast models for a given query workload and multi-dimensional data set. Finally, we extend forecast queries with continuous aspects allowing an application to register a query once at our system. As new time series values arrive, we send notifications to the application based on predefined time and accuracy constraints. All of our optimization approaches intend to increase the efficiency of forecast queries while ensuring high forecast accuracy.
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Najmizadehbaghini, Hossein. "Enhancing the Efficacy of Predictive Analytical Modeling in Operational Management Decision Making." Thesis, University of North Texas, 2019. https://digital.library.unt.edu/ark:/67531/metadc1538693/.

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In this work, we focus on enhancing the efficacy of predictive modeling in operational management decision making in two different settings: Essay 1 focuses on demand forecasting for the companies and the second study utilizes longitudinal data to analyze the illicit drug seizure and overdose deaths in the United States. In Essay 1, we utilize an operational system (newsvendor model) to evaluate the forecast method outcome and provide guidelines for forecast method (the exponential smoothing model) performance assessment and judgmental adjustments. To assess the forecast outcome, we consider not only the common forecast error minimization approach but also the profit maximization at the end of the forecast horizon. Including profit in our assessment enables us to determine if error minimization always results in maximum profit. We also look at the different levels of profit margin to analyze their impact on the forecasting method performance. Our study also investigates how different demand patterns influence maximizing the forecasting method performance. Our study shows that the exponential smoothing model family has a better performance in high-profit products, and the rate of decrease in performance versus demand uncertainty is higher in a stationary demand environment.In the second essay, we focus on illicit drug overdose death rate. Illicit drug overdose deaths are the leading cause of injury death in the United States. In 2017, overdose death reached the highest ever recorded level (70,237), and statistics show that it is a growing problem. The age adjusted rate of drug overdose deaths in 2017 (21.7 per 100,000) is 9.6% higher than the rate in 2016 (19.8 per 100,000) (U. S. Drug Enforcement Administration, 2018, p. V). Also, Marijuana consumption among youth has increased since 2009. The magnitude of the illegal drug trade and its resulting problems have led the government to produce large and comprehensive datasets on a variety of phenomena relating to illicit drugs. In this study, we utilize these datasets to examine how marijuana usage among youth influence excessive drug usage. We measure excessive drug usage in terms of drug overdose death rate per state. Our study shows that illegal marijuana consumption increases excessive drug use. Also, we analyze the pattern of most frequently seized illicit drugs and compare it with drugs that are most frequently involved in a drug overdose death. We further our analysis to study seizure patterns across layers of heroin and cocaine supply chain across states. This analysis reveals that most active layers of the heroin supply chain in the American market are retailers and wholesalers, while multi-kilo traffickers are the most active players in the cocaine supply chain. In summary, the studies in this dissertation explore the use of analytical, descriptive, and predictive models to detect patterns to improve efficacy and initiate better operational management decision making.
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Faber, Andreas D. [Verfasser], Stefan [Gutachter] Spinler, and Arnd [Gutachter] Huchzermeier. "Data analytics in supply chain planning : applications in intermittent demand forecasting, partial defection prediction and price discrimination / Andreas D. Faber ; Gutachter: Stefan Spinler, Arnd Huchzermeier." Vallendar : WHU - Otto Beisheim School of Management, 2021. http://d-nb.info/1240764359/34.

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Almamy, Jeehan. "An evaluation of Altman's Z score using cash flow ratio as analytical tool to predict corporate failure amid the recent financial crisis in the UK." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/13735.

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One of the most important threats for many firms today, despite their nature of the operation, size and longevity, is insolvency. Existing empirical evidence has shown that in the past two decades, business failures have occurred at a higher rate than any time since the 1930s. Many business failure studies have been conducted over time using financial ratios as inputs and traditional statistical techniques. Some of these studies examined whether cash flow information improves the prediction of business failure. Most recently, researchers have employed discriminant analysis to perform business failure prediction. The recent changes in the world caused by unstable environments where many firms fail more than ever, there is increasing need to predict business failure. To this date, there have been limited previous studies conducted on failure prediction for UK firms. Even in other countries, there has been a small amount of research done in the field of firm failures. Therefore, this study investigates the extension of Altman’s (1968) original model in predicting the health of UK firms using discriminant analysis and performance ratios to test which ratios are statistically significant in predicting the health of the UK firms .a selected sample containing 90 failed and 1000 non failed on UK industrial firms from 2000 – 2013. The main purpose of this study is to contribute towards Altman’s (1968) original Z-score model by adding new variables (Cash flow ratio). The study found that cash flow, when combined with Altman’s original variables is highly significant in predicting the health of UK general firms. A J-UK model was developed to test the health of UK firms. When compared with the re-estimated the Altman’s original model in the UK context, the predictive power of the model was 82.9%, which is consistent with Taffler’s (1982) UK model. Furthermore, to test the predictive power of the model before, during and after the financial crisis periods; results show that J-UK model had a higher accuracy to predict the health of UK firms than the re-estimated Altman’s original model. Finally, the study proves that liquidity, profitability, leverage and capital turnover ratios are significant ratios in predicting failure. Liquidity and profitability have the highest contribution to the results of both re-estimated Altman’s original model and J-UK model. This study has implications for decision makers. Regulatory bodies and practitioners have to take into account the ratios, which contributed highest to the model in order to serve as early warning signals for corrective action.
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Evans, Ben Richard. "Data-driven prediction of saltmarsh morphodynamics." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/276823.

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Saltmarshes provide a diverse range of ecosystem services and are protected under a number of international designations. Nevertheless they are generally declining in extent in the United Kingdom and North West Europe. The drivers of this decline are complex and poorly understood. When considering mitigation and management for future ecosystem service provision it will be important to understand why, where, and to what extent decline is likely to occur. Few studies have attempted to forecast saltmarsh morphodynamics at a system level over decadal time scales. There is no synthesis of existing knowledge available for specific site predictions nor is there a formalised framework for individual site assessment and management. This project evaluates the extent to which machine learning model approaches (boosted regression trees, neural networks and Bayesian networks) can facilitate synthesis of information and prediction of decadal-scale morphological tendencies of saltmarshes. Importantly, data-driven predictions are independent of the assumptions underlying physically-based models, and therefore offer an additional opportunity to crossvalidate between two paradigms. Marsh margins and interiors are both considered but are treated separately since they are regarded as being sensitive to different process suites. The study therefore identifies factors likely to control morphological trajectories and develops geospatial methodologies to derive proxy measures relating to controls or processes. These metrics are developed at a high spatial density in the order of tens of metres allowing for the resolution of fine-scale behavioural differences. Conventional statistical approaches, as have been previously adopted, are applied to the dataset to assess consistency with previous findings, with some agreement being found. The data are subsequently used to train and compare three types of machine learning model. Boosted regression trees outperform the other two methods in this context. The resulting models are able to explain more than 95% of the variance in marginal changes and 91% for internal dynamics. Models are selected based on validation performance and are then queried with realistic future scenarios which represent altered input conditions that may arise as a consequence of future environmental change. Responses to these scenarios are evaluated, suggesting system sensitivity to all scenarios tested and offering a high degree of spatial detail in responses. While mechanistic interpretation of some responses is challenging, process-based justifications are offered for many of the observed behaviours, providing confidence that the results are realistic. The work demonstrates a potentially powerful alternative (and complement) to current morphodynamic models that can be applied over large areas with relative ease, compared to numerical implementations. Powerful analyses with broad scope are now available to the field of coastal geomorphology through the combination of spatial data streams and machine learning. Such methods are shown to be of great potential value in support of applied management and monitoring interventions.
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Liu, De-Chian, and 劉德謙. "Real-time Intelligent Health Care Demand Forecasting based on Big Data Predictive Analytics - A Case Study." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/jv69xj.

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碩士<br>國立臺灣科技大學<br>工業管理系<br>107<br>With the evolution of the information and communication technology, following the development of Industry 4.0, Artificial Intelligence (AI), Big Data, and Cloud Computing play important roles in the smart manufacturing factory. In this thesis, we focused on the big data predictive analytics, having three properties: velocity, variety and volume in healthcare management. In this thesis, we proposed a real time intelligent medical forecasting system, which was divided into two phases. In the first phase, a Big Data approach for Medical Demand Forecasting, including several time series forecasting methods, such as weighted moving average method, exponential smoothing method and simple linear regression, to compensate the missing values. Then, applying ARIMA and BPNN to forecast the medical demand. In the second phase called the, Real-Time Big Data Predictive Analytics for Medical Referral Strategy, we focused on the patients who contracted the cardiovascular diseases and deployed the BPNN to fit the historical data to forecast that the original health center should refer patients to the designated health center according to the type of cardiovascular diseases. Furthermore, we used the data set from the ABC medical group as a case study in the field of healthcare management and this forecasting system not only used for this case data but also it could apply to other relatively data sets.
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Books on the topic "Forecasting ; Predictive Analytics"

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Predictive analytics: Microsoft Excel. Que Pub., 2013.

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Hsu, William H. Emerging methods in predictive analytics: Risk management and decision-making. Information Science Reference, 2014.

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Predictive analytics: The power to predict who will click, buy, lie, or die. Wiley, 2016.

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Predictive analytics: The power to predict who will click, buy, lie, or die. Wiley, 2013.

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Saaty, Thomas L. Prediction, projection, and forecasting: Applications of the analytic hierarchy process in economics, finance, politics, games, and sports. Kluwer Academic Publishers, 1991.

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Carlberg, Conrad. More Predictive Analytics: Microsoft Excel. Que Publishing, 2015.

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Staff, Solutions Inc, and KEATING. Forecasting and Predictive Analytics with Forecast X (tm). McGraw-Hill Education, 2018.

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Cordoba, Alberto. Understanding the Predictive Analytics Lifecycle. Wiley & Sons, Incorporated, John, 2014.

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Cordoba, Alberto. Understanding the Predictive Analytics Lifecycle. Wiley, 2014.

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Cordoba, Alberto. Understanding the Predictive Analytics Lifecycle. Wiley & Sons, Incorporated, John, 2014.

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Book chapters on the topic "Forecasting ; Predictive Analytics"

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Dinov, Ivo D. "Forecasting Numeric Data Using Regression Models." In Data Science and Predictive Analytics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72347-1_10.

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Varma, U. Abhinand, D. V. Rakesh Reddy, Prasanth Paraselli, and Joy Mustafi. "Answering Predictive Questions in Natural Language Based on Given Data for Forecasting." In Data Management, Analytics and Innovation. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5619-7_22.

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Pandey, Manish Kumar, and Prashant K. Srivastava. "A Probe into Performance Analysis of Real-Time Forecasting of Endemic Infectious Diseases Using Machine Learning and Deep Learning Algorithms." In Advanced Prognostic Predictive Modelling in Healthcare Data Analytics. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0538-3_12.

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Alankar, Bhavya, Nowsheena Yousf, and Shafqat Ul Ahsaan. "Predictive Analytics for Weather Forecasting Using Back Propagation and Resilient Back Propagation Neural Networks." In Advances in Intelligent Systems and Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9330-3_10.

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Saaty, Thomas L., and Luis G. Vargas. "The Analytic Hierarchy Process: Planning and Risk." In Prediction, Projection and Forecasting. Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-015-7952-0_2.

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Georgiou, Harris, Petros Petrou, Panagiotis Tampakis, et al. "Future Location and Trajectory Prediction." In Big Data Analytics for Time-Critical Mobility Forecasting. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45164-6_8.

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Rimal, Yagyanath. "Machine Learning Prediction of Time Series Data (Decomposition and Forecasting Methods Using R)." In Learning and Analytics in Intelligent Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42363-6_126.

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Khan, Mahnoor, Nadeem Javaid, Muhammad Nabeel Iqbal, Muhammad Bilal, Syed Farhan Ali Zaidi, and Rashid Ali Raza. "Load Prediction Based on Multivariate Time Series Forecasting for Energy Consumption and Behavioral Analytics." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93659-8_27.

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Kotu, Vijay, and Bala Deshpande. "Time Series Forecasting." In Predictive Analytics and Data Mining. Elsevier, 2015. http://dx.doi.org/10.1016/b978-0-12-801460-8.00010-0.

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McCormick, Keith, Richard Creeth, and Scott Mutchler. "3rd Order Analytics Demand Planning." In Advances in Business Strategy and Competitive Advantage. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-6477-7.ch013.

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Abstract:
It is commonly proposed that a greater number of individuals should have access to enterprise-level data, and that they should be able to analyze it readily and individually with data mining tools. Although the authors support greater use of Predictive Analytics by the enterprise, they favor more ready access to predictions, not to raw data. Forecasting is among the more difficult analytical challenges. Despite the importance of accurate forecasts, organizations often resort to the subjective judgment of a business analyst. Forecasts are also among the most widely used analytics, broadly distributed to the organization. The authors propose an approach that centralizes the forecasting activity using Predictive Analytics but preserves the wide distribution of the resulting forecast using Business Intelligence technology.
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Conference papers on the topic "Forecasting ; Predictive Analytics"

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Klünder, Jil, Oliver Karras, Fabian Kortum, and Kurt Schneider. "Forecasting Communication Behavior in Student Software Projects." In PROMISE 2016: The 12th International Conference on Predictive Models and Data Analytics in Software Engineering. ACM, 2016. http://dx.doi.org/10.1145/2972958.2972961.

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Kedia, Rishika, and Vijay Patidar. "Predictive Analytics for Storage Management Using Time-Series Forecasting Techniques." In 2019 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM). IEEE, 2019. http://dx.doi.org/10.1109/ccem48484.2019.00009.

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Faizullov, Ilias, and Sergey Yablonsky. "Modern Advanced Analytics Platforms and Predictive Models for Stock Price Forecasting: IBM Watson Analytics Case." In Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2017. http://dx.doi.org/10.24251/hicss.2017.128.

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Domingo, Annael J., Felan Carlo Garcia, Mary Lai Salvana, Nathaniel J. C. Libatique, and Gregory L. Tangonan. "Short Term Wind Speed Forecasting : A Machine Learning Based Predictive Analytics." In TENCON 2018 - 2018 IEEE Region 10 Conference. IEEE, 2018. http://dx.doi.org/10.1109/tencon.2018.8650287.

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Moon, Jihoon, Kyu-Hyung Kim, Yongsung Kim, and Eenjun Hwang. "A Short-Term Electric Load Forecasting Scheme Using 2-Stage Predictive Analytics." In 2018 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2018. http://dx.doi.org/10.1109/bigcomp.2018.00040.

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Wojtkiewicz, Jessica, Satya Katragadda, and Raju Gottumukkala. "A Concept-Drift Based Predictive-Analytics Framework: Application for Real-Time Solar Irradiance Forecasting." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622216.

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Vinayaka, Raj T., Jinka Parthasarathi, SubrahmanyaVRK Rao, and Gopinath J Mohan. "A case study on the application of predictive analytics toward forecasting swing door failure." In 2013 IEEE Conference on Open Systems (ICOS). IEEE, 2013. http://dx.doi.org/10.1109/icos.2013.6735049.

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Dabbas, Mohammad, Perambur S. Neelakanta, and Dolores DeGroff. "ANN-based predictive analytics of forecasting with sparse data: Applications in data mining contexts." In 2013 Third International Conference on Recent Trends in Information Technology (ICRTIT). IEEE, 2013. http://dx.doi.org/10.1109/icrtit.2013.6844181.

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Popp, Melanie R., Alex M. D. Renaud, and Joshua WY Lee. "A New Method for Production Forecasting: Predictive Analytics vs. Conventional Methods in the Montney." In Unconventional Resources Technology Conference. American Association of Petroleum Geologists, 2019. http://dx.doi.org/10.15530/urtec-2019-264.

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Ayyanathan, N., and A. Kannammal. "Combined forecasting and cognitive Decision Support System for Indian green coffee supply chain predictive analytics." In 2015 International Conference on Cognitive Computing and Information Processing (CCIP). IEEE, 2015. http://dx.doi.org/10.1109/ccip.2015.7100735.

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