Relevant bibliographies by topics / Prediction

Academic literature on the topic 'Prediction'

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Published: 4 June 2021
Last updated: 31 August 2024

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Journal articles on the topic "Prediction"

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Mtimkulu, Zimele, and Mfowabo Maphosa. "Flight Delay Prediction Using Machine Learning: A Comparative Study of Ensemble Techniques." International Conference on Artificial Intelligence and its Applications 2023 (November 9, 2023): 212–18. http://dx.doi.org/10.59200/icarti.2023.030.

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Machine learning is a promising tool for predicting flight delays. Accurately predicting flight delays in aviation enhances operational efficiency and passenger contentment. Accurate predictions are critical to improving operational efficiency and passenger satisfaction. The study aims to develop a robust predictive model for domestic flights and identify key variables affecting delays. This investigation transcends the confines of traditional prediction methodologies by embracing the potency of ensemble techniques, thereby imbuing the model with the capacity to capture intricate patterns and dependencies within the dataset holistically. By adopting a comparative approach, this study systematically evaluates a spectrum of ensemble methods, unravelling their strengths and weaknesses in the context of flight delay prediction. The study’s results highlight the strong predictive performance of stacking methods (92.4%) and random forest (91.2%), which effectively capture patterns while cautioning about the sensitivity of AdaBoostClassifier (51.6%) to noisy data. This research has the potential to augment the precision and applicability of flight delay prediction, fostering operational enhancements within the aviation industry while increasing passenger satisfaction.
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Carlsson, Leo S., Mikael Vejdemo-Johansson, Gunnar Carlsson, and Pär G. Jönsson. "Fibers of Failure: Classifying Errors in Predictive Processes." Algorithms 13, no. 6 (June 23, 2020): 150. http://dx.doi.org/10.3390/a13060150.

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Predictive models are used in many different fields of science and engineering and are always prone to make faulty predictions. These faulty predictions can be more or less malignant depending on the model application. We describe fibers of failure (FiFa), a method to classify failure modes of predictive processes. Our method uses Mapper, an algorithm from topological data analysis (TDA), to build a graphical model of input data stratified by prediction errors. We demonstrate two ways to use the failure mode groupings: either to produce a correction layer that adjusts predictions by similarity to the failure modes; or to inspect members of the failure modes to illustrate and investigate what characterizes each failure mode. We demonstrate FiFa on two scenarios: a convolutional neural network (CNN) predicting MNIST images with added noise, and an artificial neural network (ANN) predicting the electrical energy consumption of an electric arc furnace (EAF). The correction layer on the CNN model improved its prediction accuracy significantly while the inspection of failure modes for the EAF model provided guiding insights into the domain-specific reasons behind several high-error regions.
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Yang, Ke. "Predicting Student Performance Using Artificial Neural Networks." Journal of Arts, Society, and Education Studies 6, no. 1 (May 15, 2024): 45–77. http://dx.doi.org/10.69610/j.ases.20240515.

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<p class="MsoNormal" style="text-align: justify;"><span style="font-family: Times New Roman;">This paper explores machine learning approaches to predicting student performance using artificial neural networks. By employing educational data mining and predictive modeling techniques, accurate predictions of student outcomes were achieved. The results indicate that artificial neural networks exhibit high accuracy and reliability in forecasting student academic performance. Through comprehensive analysis and empirical testing, this approach significantly enhances the effectiveness of student performance predictions. Future research directions may include further optimization of the model's algorithms and expansion of the data sample size to improve prediction accuracy and applicability. The method demonstrated exceptional performance in predicting student outcomes, offering high accuracy and efficacy. By mining and analyzing extensive educational data, a predictive model was established and validated through experiments. We introduce a novel predictive model to the field of education, providing robust support for student learning and educational decision-making. Future enhancements can optimize the model, increase prediction precision, and expand application fields to better serve the development of educational endeavors.</span></p>
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Wang, Hsin-Yao, Yu-Hsin Liu, Yi-Ju Tseng, Chia-Ru Chung, Ting-Wei Lin, Jia-Ruei Yu, Yhu-Chering Huang, and Jang-Jih Lu. "Investigating Unfavorable Factors That Impede MALDI-TOF-Based AI in Predicting Antibiotic Resistance." Diagnostics 12, no. 2 (February 5, 2022): 413. http://dx.doi.org/10.3390/diagnostics12020413.

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The combination of Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) spectra data and artificial intelligence (AI) has been introduced for rapid prediction on antibiotic susceptibility testing (AST) of Staphylococcus aureus. Based on the AI predictive probability, cases with probabilities between the low and high cut-offs are defined as being in the “grey zone”. We aimed to investigate the underlying reasons of unconfident (grey zone) or wrong predictive AST. In total, 479 S. aureus isolates were collected and analyzed by MALDI-TOF, and AST prediction and standard AST were obtained in a tertiary medical center. The predictions were categorized as correct-prediction group, wrong-prediction group, and grey-zone group. We analyzed the association between the predictive results and the demographic data, spectral data, and strain types. For methicillin-resistant S. aureus (MRSA), a larger cefoxitin zone size was found in the wrong-prediction group. Multilocus sequence typing of the MRSA isolates in the grey-zone group revealed that uncommon strain types comprised 80%. Of the methicillin-susceptible S. aureus (MSSA) isolates in the grey-zone group, the majority (60%) comprised over 10 different strain types. In predicting AST based on MALDI-TOF AI, uncommon strains and high diversity contribute to suboptimal predictive performance.
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Pace, Michael L. "Prediction and the aquatic sciences." Canadian Journal of Fisheries and Aquatic Sciences 58, no. 1 (January 1, 2001): 63–72. http://dx.doi.org/10.1139/f00-151.

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The need for prediction is now widely recognized and frequently articulated as an objective of research programs in aquatic science. This recognition is partly the legacy of earlier advocacy by the school of empirical limnologists. This school, however, presented prediction narrowly and failed to account for the diversity of predictive approaches as well to set prediction within the proper scientific context. Examples from time series analysis and probabilistic models oriented toward management provide an expanded view of approaches and prospects for prediction. The context and rationale for prediction is enhanced understanding. Thus, prediction is correctly viewed as an aid to building scientific knowledge with better understanding leading to improved predictions. Experience, however, suggests that the most effective predictive models represent condensed models of key features in aquatic systems. Prediction remains important for the future of aquatic sciences. Predictions are required in the assessment of environmental concerns and for testing scientific fundamentals. Technology is driving enormous advances in the ability to study aquatic systems. If these advances are not accompanied by improvements in predictive capability, aquatic research will have failed in delivering on promised objectives. This situation should spark discomfort in aquatic scientists and foster creative approaches toward prediction.
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Фокина, Элла, and Георгий Елизарьев. "M&A Prediction Model-Based Investment Strategies." Journal of Corporate Finance Research / Корпоративные Финансы | ISSN: 2073-0438 17, no. 2 (September 4, 2023): 5–26. http://dx.doi.org/10.17323/j.jcfr.2073-0438.17.2.2023.5-26.

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In this paper, we study the development of investment strategies by predicting M&A deals using a logistic model with the financial and non-financial indicators of public companies. A random sample of 1510 acquired and non-acquired companies in Germany, the United Kingdom, France, Sweden, and Russia over the period 2000-2021 was used to design an M&A logit prediction model with high predictive power. The use of interaction variables significantly improved the model’s predictive power and allowed it to obtain more than 70% of correct out-of-sample predictions. Then the model’s ability to generate abnormal returns was tested with the help of an event study using share price data over the period 2011-2021. We show that an M&A prediction model can also efficiently generate abnormal returns (up to 49% on average) for a portfolio of companies that are expected to be acquired. Moreover, we uncover evidence that reduction in false positiveand negative predictions has a positive effect on abnormal returns due to the added model flexibility resulting from interaction terms. Our positive theoretical and empirical results can help both private and institutional investors to design investment strategies. In addition, there are indirect implications that support the practical importance of an efficient M&A prediction model.
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Burbey, Ingrid, and Thomas L. Martin. "A survey on predicting personal mobility." International Journal of Pervasive Computing and Communications 8, no. 1 (March 30, 2012): 5–22. http://dx.doi.org/10.1108/17427371211221063.

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PurposeLocation‐prediction enables the next generation of location‐based applications. The purpose of this paper is to provide a historical summary of research in personal location‐prediction. Location‐prediction began as a tool for network management, predicting the load on particular cellular towers or WiFi access points. With the increasing popularity of mobile devices, location‐prediction turned personal, predicting individuals' next locations given their current locations.Design/methodology/approachThis paper includes an overview of prediction techniques and reviews several location‐prediction projects comparing the raw location data, feature extraction, choice of prediction algorithms and their results.FindingsA new trend has emerged, that of employing additional context to improve or expand predictions. Incorporating temporal information enables location‐predictions farther out into the future. Appending place types or place names can improve predictions or develop prediction applications that could be used in any locale. Finally, the authors explore research into diverse types of context, such as people's personal contacts or health activities.Originality/valueThis overview provides a broad background for future research in prediction.
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Bierkens, M. F. P., and L. P. H. van Beek. "Seasonal Predictability of European Discharge: NAO and Hydrological Response Time." Journal of Hydrometeorology 10, no. 4 (August 1, 2009): 953–68. http://dx.doi.org/10.1175/2009jhm1034.1.

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Abstract In this paper the skill of seasonal prediction of river discharge and how this skill varies between the branches of European rivers across Europe is assessed. A prediction system of seasonal (winter and summer) discharge is evaluated using 1) predictions of the average North Atlantic Oscillation (NAO) index for the coming winter based on May SST anomalies of the North Atlantic; 2) a global-scale hydrological model; and 3) 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) data. The skill of seasonal discharge predictions is investigated with a numerical experiment. Also Europe-wide patterns of predictive skill are related to the use of NAO-based seasonal weather prediction, the hydrological properties of the river basin, and a correct assessment of initial hydrological states. These patterns, which are also corroborated by observations, show that in many parts of Europe the skill of predicting winter discharge can, in theory, be quite large. However, this achieved skill mainly comes from knowing the correct initial conditions of the hydrological system (i.e., groundwater, surface water, soil water storage of the basin) rather than from the use of NAO-based seasonal weather prediction. These factors are equally important for predicting subsequent summer discharge.
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Juneja, Dr Sonia. "House Price Prediction Using Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 3156–64. http://dx.doi.org/10.22214/ijraset.2023.54259.

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Abstract: House price prediction is the process of using learning based techniques to predict the future sale price of a house. It explores the use of predictive models to accurately forecast house prices. It also examines the effectiveness of using machine learning algorithms to predict house prices. In particular, our research investigates the impact of data such as location, duration of house, dimension of house on the accuracy of the predictions. Finally, a discussion on the implications of using machine learning algorithms for predicting price for consumers and real estate professionals is presented
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Wei, Chih-Chiang, and Wei-Jen Kao. "Establishing a Real-Time Prediction System for Fine Particulate Matter Concentration Using Machine-Learning Models." Atmosphere 14, no. 12 (December 13, 2023): 1817. http://dx.doi.org/10.3390/atmos14121817.

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With the rapid urbanization and industrialization in Taiwan, pollutants generated from industrial processes, coal combustion, and vehicle emissions have led to severe air pollution issues. This study focuses on predicting the fine particulate matter (PM2.5) concentration. This enables individuals to be aware of their immediate surroundings in advance, reducing their exposure to high concentrations of fine particulate matter. The research area includes Keelung City and Xizhi District in New Taipei City, located in northern Taiwan. This study establishes five fine prediction models based on machine-learning algorithms, namely, the deep neural network (DNN), M5’ decision tree algorithm (M5P), M5’ rules decision tree algorithm (M5Rules), alternating model tree (AMT), and multiple linear regression (MLR). Based on the predictive results from these five models, the study evaluates the optimal model for forecast horizons and proposes a real-time PM2.5 concentration prediction system by integrating various models. The results demonstrate that the prediction errors vary across different models at different forecast horizons, with no single model consistently outperforming the others. Therefore, the establishment of a hybrid prediction system proves to be more accurate in predicting future PM2.5 concentration compared to a single model. To assess the practicality of the system, the study process involved simulating data, with a particular focus on the winter season when high PM2.5 concentrations are prevalent. The predictive system generated excellent results, even though errors increased in long-term predictions. The system can promptly adjust its predictions over time, effectively forecasting the PM2.5 concentration for the next 12 h.
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Dissertations / Theses on the topic "Prediction"

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Carrión, Brännström Robin. "Aggregating predictions using Non-Disclosed Conformal Prediction." Thesis, Uppsala universitet, Statistiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385098.

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When data are stored in different locations and pooling of such data is not allowed, there is an informational loss when doing predictive modeling. In this thesis, a new method called Non-Disclosed Conformal Prediction (NDCP) is adapted into a regression setting, such that predictions and prediction intervals can be aggregated from different data sources without interchanging any data. The method is built upon the Conformal Prediction framework, which produces predictions with confidence measures on top of any machine learning method. The method is evaluated on regression benchmark data sets using Support Vector Regression, with different sizes and settings for the data sources, to simulate real life scenarios. The results show that the method produces conservatively valid prediction intervals even though in some settings, the individual data sources do not manage to create valid intervals. NDCP also creates more stable intervals than the individual data sources. Thanks to its straightforward implementation, data owners which cannot share data but would like to contribute to predictive modeling, would benefit from using this method.
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Miller, Mark Daniel. "Entangled predictive brain : emotion, prediction and embodied cognition." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/33218.

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How does the living body impact, and perhaps even help constitute, the thinking, reasoning, feeling agent? This is the guiding question that the following work seeks to answer. The subtitle of this project is emotion, prediction and embodied cognition for good reason: these are the three closely related themes that tie together the various chapters of the following thesis. The central claim is that a better understanding of the nature of emotion offers valuable insight for understanding the nature of the so called 'predictive mind', including a powerful new way to think about the mind as embodied Recently a new perspective has arguably taken the pole position in both philosophy of mind and the cognitive sciences when it comes to discussing the nature of mind. This framework takes the brain to be a probabilistic prediction engine. Such engines, so the framework proposes, are dedicated to the task of minimizing the disparity between how they expect the world to be and how the world actually is. Part of the power of the framework is the elegant suggestion that much of what we take to be central to human intelligence - perception, action, emotion, learning and language - can be understood within the framework of prediction and error reduction. In what follows I will refer to this general approach to understanding the mind and brain as 'predictive processing'. While the predictive processing framework is in many ways revolutionary, there is a tendency for researchers interested in this topic to assume a very traditional 'neurocentric' stance concerning the mind. I argue that this neurocentric stance is completely optional, and that a focus on emotional processing provides good reasons to think that the predictive mind is also a deeply embodied mind. The result is a way of understanding the predictive brain that allows the body and the surrounding environment to make a robust constitutive contribution to the predictive process. While it's true that predictive models can get us a long way in making sense of what drives the neural-economy, I will argue that a complete picture of human intelligence requires us to also explore the many ways that a predictive brain is embodied in a living body and embedded in the social-cultural world in which it was born and lives.
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Björsell, Joachim. "Long Range Channel Predictions for Broadband Systems : Predictor antenna experiments and interpolation of Kalman predictions." Thesis, Uppsala universitet, Signaler och System, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-281058.

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The field of wireless communication is under massive development and the demands on the cellular system, especially, are constantly increasing as the utilizing devices are increasing in number and diversity. A key component of wireless communication is the knowledge of the channel, i.e, how the signal is affected when sent over the wireless medium. Channel prediction is one concept which can improve current techniques or enable new ones in order to increase the performance of the cellular system. Firstly, this report will investigate the concept of a predictor antenna on new, extensive measurements which represent many different environments and scenarios. A predictor antenna is a separate antenna that is placed in front of the main antenna on the roof of a vehicle. The predictor antenna could enable good channel prediction for high velocity vehicles. The measurements show to be too noisy to be used directly in the predictor antenna concept but show potential if the measurements can be noise-filtered without distorting the signal. The use of low-pass filter and Kalman filter to do this, did not give the desired results but the technique to do this should be further investigated. Secondly, a interpolation technique will be presented which utilizes predictions with different prediction horizon by estimating intermediate channel components using interpolation. This could save channel feedback resources as well as give a better robustness to bad channel predictions by letting fresh, local, channel predictions be used as quality reference of the interpolated channel estimates. For a linear interpolation between 8-step and 18-step Kalman predictions with Normalized Mean Square Error (NMSE) of -15.02 dB and -10.88 dB, the interpolated estimates had an average NMSE of -13.14 dB, while lowering the required feedback data by about 80 %. The use of a warning algorithm reduced the NMSE by a further 0.2 dB. It mainly eliminated the largest prediction error which otherwise could lead to retransmission, which is not desired.
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Bramlet, John. "Earthquake prediction and earthquake damage prediction /." Connect to resource, 1996. http://hdl.handle.net/1811/31764.

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Greco, Antonino. "The role of task relevance in the modulation of brain dynamics during sensory predictions." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/307050.

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Associative learning is a fundamental ability biological systems possess in order to adapt to a nonstationary environment. One of the core aspects of associative learning theoretical frameworks is that surprising events drive learning by signalling the need to update the system’s beliefs about the probability structure governing stimuli associations. Specifically, the central neural system generates internal predictions to anticipate the causes of its perceptual experience and compute a prediction error to update its generative model of the environment, an idea generally known as the predictive coding framework. However, it is not clear whether the brain generates these predictions only for goal-oriented behavior or they are more a general characteristic of the brain function. In this thesis, I explored the role of task relevance in modulating brain activity when exposed to sensory associative learning task. In the first study, participants were asked to perform a perceptual detection task while audio-visual stimuli were presented as distractors. These distractors possessed a probability structure that made some of them more paired than others. Results showed that occipital activity triggered by the conditioned stimulus was elicited just before the arrival of the unconditioned visual stimulus. Moreover, occipital activity after the onset of the unconditioned stimulus followed a pattern of precision-weighted prediction errors. In the second study, two more sessions were added to the task in the previous study in which the probability structure for all stimuli associations was identical and the whole experiment was spanned in six days across two weeks. Results showed a difference in the modulation of the beta band induced by the presentation of the unconditioned stimulus preceded by the predictive and unpredictive conditioned auditory stimuli by comparing the pre and post sessions activity. In the third study, participants were exposed to a similar task with respect to the second study with the modification that there was a condition in which the conditioned-unconditioned stimulus association was task-relevant, thus allowing to directly compare task-relevant and task-irrelevant associations. Results showed that both types of associations had similar patterns in terms of activity and functional connectivity when comparing the brain responses to the onset of the unconditioned visual stimulus. Taken together, these findings demonstrate irrelevant associations rely on the same neural mechanisms of relevant ones. Thus, even if task relevance plays a modulatory role on the strength of the neural effects of associative learning, predictive processes take place in sensory associative learning regardless of task relevance.
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Kock, Peter. "Prediction and predictive control for economic optimisation of vehicle operation." Thesis, Kingston University, 2013. http://eprints.kingston.ac.uk/35861/.

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Truck manufacturers are currently under pressure to reduce pollution and cost of transportation. The cost efficient way to reduce CO[sub]2 and cost is to reduce fuel consumption by adaptation of the vehicle speed to the driving conditions - by heuristic knowledge or mathematical optimisation. Due to their experience, professional drivers are capable of driving with great efficiency in terms of fuel consumption. The key research question addressed in this work is the comparison of the fuel efficiency for an unassisted drive by an experienced professional driver versus an enhanced drive using driver assistance system. The motivation for this is based on the advantage of such a system in terms of price (lower than driver's training) but potentially it can be challenging to obtain drivers' acceptance of the system. There is a range of fundamental issued that have to be addressed prior to the design and implementation of the driver assistance system. The first issue is related to the evaluation of the correctness of the prediction model under development, due to a range of inaccuracies introduced by slope errors in digital maps, imprecise modelling of combustion engine, vehicle physics etc. The second issue is related to the challenge in selecting a suitable method for optimisation of mixed integer non-linear systems. Dynamic Programming proved to be very suitable for this work and some methods of search space reduction are presented here. Also an analytical solution of the Bernoulli differential equation of the vehicle dynamics is presented and used here in order to reduce computing effort. Extensive simulation and driving tests were performed using different driving approaches to compare well trained human experts with a range of different driving assistance systems based on standard cruise control, heuristic and mathematical optimisation. Finally the acceptance of the systems by drivers been evaluated.
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Andeta, Jemal Ahmed. "Road-traffic accident prediction model : Predicting the Number of Casualties." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20146.

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Efficient and effective road traffic prediction and management techniques are crucial in intelligent transportation systems. It can positively influence road advancement, safety enhancement, regulation formulation, and route planning to save living things in advance from road traffic accidents. This thesis considers road safety by predicting the number of casualties if an accident occurs using multiple traffic accident attributes. It helps individuals (drivers) or traffic offices to adjust and control their contributions for the occurrence of an accident before emerging it. Three candidate algorithms from different regression fit patterns are proposed and evaluated to conduct the thesis: the bagging, linear, and non-linear fitting patterns. The gradient boosting machines (GBoost) from the bagging, Linearsupport vector regression (LinearSVR) from the linear, and extreme learning machines (ELM) also from the non-linear side are the selected algorithms. RMSE and MAE performance evaluation metrics are applied to evaluate the models. The GBoost achieved a better performance than the other two with a low error rate and minimum prediction interval value for 95% prediction interval. A SHAP (SHapley Additive exPlanations) interpretation technique is applied to interpret each model at the global interpretation level using SHAP’s beeswarm plots. Finally, suggestions for future improvements are presented via the dataset and hyperparameter tuning.
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Peterson, Ashley Thomas. "Cavitation prediction." Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612813.

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Åkermark, Alexander, and Mattias Hallefält. "Churn Prediction." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-41236.

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Churn analysis is an important tool for companies as it can reduce the costs that are related to customer churn. Churn prediction is the process of identifying users before they churn, this is done by implementing methods on collected data in order to find patterns that can be helpful when predicting new churners in the future.The objective of this report is to identify churners with the use of surveys collected from different golfclubs, their members and guests. This was accomplished by testing several different supervised machine learning algorithms in order to find the different classes and to see which supervised algorithms are most suitable for this kind of data.The margin of success was to have a greater accuracy than the percentage of major class in the datasetThe data was processed using label encoding, ONE-hot encoding and principal component analysis and was split into 10 folds, 9 training folds and 1 testing fold ensuring cross validation when iterated 10 times rearranging the test and training folds. Each algorithm processed the training data to create a classifier which was tested on the test data.The classifiers used for the project was K nearest neighbours, Support vector machine, multi-layer perceptron, decision trees and random forest.The different classifiers generally had an accuracy of around 72% and the best classifier which was random forest had an accuracy of 75%. All the classifiers had an accuracy above the margin of success.K-folding, confusion-matrices, classification report and other internal crossvalidation techniques were performed on the the data to ensure the quality of the classifier.The project was a success although there is a strong belief that the bottleneck for the project was the quality of the data in terms of new legislation when collecting and storing data that results in redundant and faulty data.
Churn analys är ett viktigt verktyg för företag då det kan reducera kostnaderna som är relaterade till kund churn. Churn prognoser är processen av att identifiera användare innan de churnas, detta är gjort med implementering av metoder på samlad data för att hitta mönster som är hjälpsamma när framtida användare ska prognoseras. Objektivet med denna rapport är att identifiera churnare med användning av enkäter samlade från golfklubbar och deras kunder och gäster. Det är uppnå att igenom att testa flera olika kontrollerade maskinlärnings algoritmer för att jämföra vilken algoritm som passar bäst. Felmarginalen uppgick till att ha en större träffsäkerhet än procenthalten av den dominanta klassen i datasetet. Datan behandlades med label encoding, ONE-hot encoding och principial komponent analys och delades upp i 10 delar, 9 träning och 1 test del för att säkerställa korsvalidering. Varje algoritm behandlade träningsdatan för att skapa att klassifierare som sedan testades på test datan. Klassifierarna som användes för projekted innefattar K nearest neighbours, Support vector machine, multi-layer perceptron, decision trees och random forest. De olika klassifierarna hade en generell träffssäkerhet omkring 72%, där den bästa var random forest med en träffssäkerhet på 75%. Alla klassifierare hade en träffsäkerhet än den felmarginal som st¨alldes. K-folding, confusion matrices, classification report och andra interna korsvaliderings tekniker användes för att säkerställa kvaliteten på klassifieraren. Projektet var lyckat, men det finns misstanke om att flaskhalsen för projektet låg inom kvaliteten på datan med hänsyn på villkor för ny lagstiftning vid insamling och lagring av data som leder till överflödiga och felaktiga uppgifter.
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Jahedpari, Fatemeh. "Artificial prediction markets for online prediction of continuous variables." Thesis, University of Bath, 2016. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.690730.

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In this dissertation, we propose an online machine learning technique – named Artificial Continuous Prediction Market (ACPM) – to predict the value of a continuous variable by (i) integrating a set of data streams from heterogeneous sources with time varying compositions such as changing the quality of data streams, (ii) integrating the results of several analysis models for each data source when the most suitable model for a given data source is not known a priori, (iii) dynamically weighting the prediction of each analysis model and data source to form the system prediction. We adapt the concept of prediction market, motivated by their success in forecasting accurately the outcome of many events [Nikolova and Sami, 2007]. Our proposed model instantiates a sequence of prediction markets in which artificial agents play the role of market participants. Agents participate in the markets with the objective of increasing their own utility and hence indirectly cause the markets to aggregate their knowledge. Each market is run in a number of rounds in which agents have the opportunity to send their prediction and bet to the market. At the end of each round, the aggregated prediction of the crowd is announced to all agents, which provides a signal to agents about the private information of other agents so they can adjust their beliefs accordingly. Once the true value of the record is known, agents are rewarded according to accuracy of their prediction. Using this information, agents update their models and knowledge, with the aim of improving their performance in future markets. This thesis proposes two trading strategies to be utilised by agents when participating in a market. While the first one is a naive constant strategy, the second one is an adaptive strategy based on Q-Learning technique [Watkins, 1989]. We evaluate the performance of our model in different situations using real-world and synthetic data sets. Our results suggest that ACPM: i) is either better or very close to the best performing agents, ii) is resilient to the addition of agents with low performance, iii) outperforms many well-known machine learning models, iv) is resilient to quality drop-out in the best performing agents, v) adapts to changes in quality of agents predictions.
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Books on the topic "Prediction"

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Kanjilal, P. P. Adaptive prediction and predictive control. Stevenage, Herts., U.K: P. Peregrinus on behalf of Institution of Electrical Engineers, 1995.

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Engineers, Institution of Electrical, ed. Adaptive prediction and predictive control. Stevenage: P. Peregrinus on behalf of Institution of Electrical Engineers, 1995.

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Manski, Charles F. Interpreting the predictions of prediction markets. Cambridge, MA: National Bureau of Economic Research, 2004.

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Ma, Zongjin, Zhengxiang Fu, Yingzhen Zhang, Chengmin Wang, Guomin Zhang, and Defu Liu. Earthquake Prediction. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-61269-5.

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Peijnenburg, Willie J. G. M., and Jirí Damborský, eds. Biodegradability Prediction. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-011-5686-8.

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Kollmar, Martin, ed. Gene Prediction. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9173-0.

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Shimazaki, Kunihiko, and William Stuart, eds. Earthquake Prediction. Basel: Birkhäuser Basel, 1985. http://dx.doi.org/10.1007/978-3-0348-6245-5.

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Wyatt, Ray. Plan Prediction. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-46430-5.

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Luckner, Stefan, Jan Schröder, Christian Slamka, Markus Franke, Andreas Geyer-Schulz, Bernd Skiera, Martin Spann, and Christof Weinhardt. Prediction Markets. Wiesbaden: Gabler Verlag, 2012. http://dx.doi.org/10.1007/978-3-8349-7085-5.

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Wolfers, Justin. Prediction markets. Cambridge, MA: National Bureau of Economic Research, 2004.

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Book chapters on the topic "Prediction"

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Pourbafrani, Mahsa, Shreya Kar, Sebastian Kaiser, and Wil M. P. van der Aalst. "Remaining Time Prediction for Processes with Inter-case Dynamics." In Lecture Notes in Business Information Processing, 140–53. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_11.

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AbstractProcess mining techniques use event data to describe business processes, where the provided insights are used for predicting processes’ future states (Predictive Process Monitoring). Remaining Time Prediction of process instances is an important task in the field of Predictive Process Monitoring (PPM). Existing approaches have two key limitations in developing Remaining Time Prediction Models (RTM): (1) The features used for predictions lack process context, and the created models are black-boxes. (2) The process instances are considered to be in isolation, despite the fact that process states, e.g., the number of running instances, influence the remaining time of a single process instance. Recent approaches improve the quality of RTMs by utilizing process context related to batching-at-end inter-case dynamics in the process, e.g., using the time to batching as a feature. We propose an approach that decreases the previous approaches’ reliance on user knowledge for discovering fine-grained process behavior. Furthermore, we enrich our RTMs with the extracted features for multiple performance patterns (caused by inter-case dynamics), which increases the interpretability of models. We assess our proposed remaining time prediction method using two real-world event logs. Incorporating the created inter-case features into RTMs results in more accurate and interpretable predictions.
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Liu, Wendi, Léan E. Garland, Jesus Ochoa, and Michael J. Pyrcz. "A Geostatistical Heterogeneity Metric for Spatial Feature Engineering." In Springer Proceedings in Earth and Environmental Sciences, 3–19. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19845-8_1.

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AbstractHeterogeneity is a vital spatial feature for subsurface resource recovery predictions, such as mining grade tonnage functions, hydrocarbon recovery factor, and water aquifer draw-down predictions. Feature engineering presents the opportunity to integrate heterogeneity information, but traditional heterogeneity engineered features like Dykstra-Parsons and Lorenz coefficients ignore the spatial context; therefore, are not sufficient to quantify the heterogeneity over multiple scales of spatial intervals to inform predictive machine learning models. We propose a novel use of dispersion variance as a spatial-engineered feature that accounts for heterogeneity within the spatial context, including spatial continuity and sample data and model volume support size to improve predictive machine-learning-based models, e.g., for pre-drill prediction and uncertainty quantification. Dispersion variance is a generalized form of variance that accounts for volume support size and can be calculated from the semivariogram-based spatial continuity model. We demonstrate dispersion variance as a useful predictor feature for the case of hydrocarbon recovery prediction, with the ability to quantify the spatial variation over the support size of the production well drainage radius, given the spatial continuity from the variogram and trajectory of the well. We include a synthetic example based on geostatistical models and flow simulation to show the sensitivity of dispersion variance to production. Then we demonstrate the dispersion variance as an informative predictor feature for production forecasting with a field case study in the Duvernay formation.
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Fani Sani, Mohammadreza, Mozhgan Vazifehdoostirani, Gyunam Park, Marco Pegoraro, Sebastiaan J. van Zelst, and Wil M. P. van der Aalst. "Event Log Sampling for Predictive Monitoring." In Lecture Notes in Business Information Processing, 154–66. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_12.

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AbstractPredictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. This paper proposes an instance selection procedure that allows sampling training process instances for prediction models. We show that our sampling method allows for a significant increase of training speed for next activity prediction methods while maintaining reliable levels of prediction accuracy.
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Spenrath, Yorick, Marwan Hassani, and Boudewijn F. van Dongen. "Online Prediction of Aggregated Retailer Consumer Behaviour." In Lecture Notes in Business Information Processing, 211–23. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_16.

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AbstractPredicting the behaviour of consumers provides valuable information for retailers, such as the expected spend of a consumer or the total turnover of the retailer. The ability to make predictions on an individual level is useful, as it allows retailers to accurately perform targeted marketing. However, with the expected large number of consumers and their diverse behaviour, making accurate predictions on an individual consumer level is difficult. In this paper we present a framework that focuses on this trade-off in an online setting. By making predictions on a larger number of consumers at a time, we improve the predictive accuracy but at the cost of usefulness, as we can say less about the individual consumers. The framework is developed in an online setting, where we update the prediction model and make new predictions over time. We show the existence of the trade-off in an experimental evaluation on a real-world dataset consisting of 39 weeks of transaction data.
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Qian, Shenghua. "Vehicle Collision Prediction Model on the Internet of Vehicles." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 518–30. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_53.

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AbstractAn active collision prediction model on the Internet of Vehicles is proposed. Through big data calculation on the cloud computing platform, the model predicts whether the vehicles may collide and the time of the collision, so the server actively sends warning signals to the vehicles that may collide. Firstly, the vehicle collision prediction model preprocesses the data set, and then constructs a new feature set through feature engineering. For the imbalance of the data set, which affects predictive results, SMOTE algorithm is proposed to generate new samples. Then, the LightGBM algorithm optimized by Bayesian parameters is used to predict the vehicle collision state. Finally, for the problem of low accuracy in predicting the collision time, the time prediction is transformed into a classification problem, and the Bayesian optimization K-means algorithm is used to predict the vehicle collision time. The experimental results prove that the vehicle collision prediction model proposed in this paper has better results.
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Luckner, Stefan, Jan Schröder, Christian Slamka, Markus Franke, Andreas Geyer-Schulz, Bernd Skiera, Martin Spann, and Christof Weinhardt. "Introduction." In Prediction Markets, 1–5. Wiesbaden: Gabler Verlag, 2012. http://dx.doi.org/10.1007/978-3-8349-7085-5_1.

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Luckner, Stefan, Jan Schröder, Christian Slamka, Markus Franke, Andreas Geyer-Schulz, Bernd Skiera, Martin Spann, and Christof Weinhardt. "Fundamentals of Prediction Markets." In Prediction Markets, 6–10. Wiesbaden: Gabler Verlag, 2012. http://dx.doi.org/10.1007/978-3-8349-7085-5_2.

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Luckner, Stefan, Jan Schröder, Christian Slamka, Markus Franke, Andreas Geyer-Schulz, Bernd Skiera, Martin Spann, and Christof Weinhardt. "Key Design Elements of Prediction Markets." In Prediction Markets, 11–47. Wiesbaden: Gabler Verlag, 2012. http://dx.doi.org/10.1007/978-3-8349-7085-5_3.

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Luckner, Stefan, Jan Schröder, Christian Slamka, Markus Franke, Andreas Geyer-Schulz, Bernd Skiera, Martin Spann, and Christof Weinhardt. "Applications of Prediction Markets." In Prediction Markets, 48–117. Wiesbaden: Gabler Verlag, 2012. http://dx.doi.org/10.1007/978-3-8349-7085-5_4.

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Luckner, Stefan, Jan Schröder, Christian Slamka, Markus Franke, Andreas Geyer-Schulz, Bernd Skiera, Martin Spann, and Christof Weinhardt. "Conclusion." In Prediction Markets, 118–19. Wiesbaden: Gabler Verlag, 2012. http://dx.doi.org/10.1007/978-3-8349-7085-5_5.

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Conference papers on the topic "Prediction"

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Deshwal, Aryan, Janardhan Rao Doppa, and Dan Roth. "Learning and Inference for Structured Prediction: A Unifying Perspective." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/878.

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In a structured prediction problem, one needs to learn a predictor that, given a structured input, produces a structured object, such as a sequence, tree, or clustering output. Prototypical structured prediction tasks include part-of-speech tagging (predicting POS tag sequence for an input sentence) and semantic segmentation of images (predicting semantic labels for pixels of an input image). Unlike simple classification problems, here there is a need to assign values to multiple output variables accounting for the dependencies between them. Consequently, the prediction step itself (aka ``inference" or ``decoding") is computationally-expensive, and so is the learning process, that typically requires making predictions as part of it. The key learning and inference challenge is due to the exponential size of the structured output space and depend on its complexity. In this paper, we present a unifying perspective of the different frameworks that address structured prediction problems and compare them in terms of their strengths and weaknesses. We also discuss important research directions including integration of deep learning advances into structured prediction, and learning from weakly supervised signals and active querying to overcome the challenges of building structured predictors from small amount of labeled data.
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Jørgensen, Magne, and Bjørn Faugli. "Prediction of Overoptimistic Predictions." In 10th International Conference on Evaluation and Assessment in Software Engineering (EASE). BCS Learning & Development, 2006. http://dx.doi.org/10.14236/ewic/ease2006.5.

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Jakša, Rudolf, Martina Zeleňáková, Juraj Koščák, and Helena Hlavatá. "Local Prediction of Precipitation Based on Neural Network." In Environmental Engineering. VGTU Technika, 2017. http://dx.doi.org/10.3846/enviro.2017.079.

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The paper is focused on analysis of local neural network model of precipitation. We use basic multilayer perceptron neural network with the time-window on input data to predict the precipitation. We predict the precipitation in the next day from the local meteorological data from past days. Data from the past 60 years were used to train the predictor. Obtained prediction model is specific for given area of Košice City in Slovakia, as the prediction is based on the statistics of the weather in given area. This precipitation predictor is multiple-input-single-output architecture with a single value per day resolution on output. Obtained results show that good local temperature prediction accuracy is possible with chosen setup, but it is worse for the precipitation prediction. Also the training requirements of precipitation predictor seem to be significantly higher then for the temperature predictor. Obtained prediction results can be used for applications based on local meteorological station data, although they are not as accurate as the state of art agency predictions based on satellite data. In the paper we will analyze design of the precipitation predictor based on existing design of the temperature predictor and provide the reader with recommended setup of such predictor for application with his/her local precipitation data.
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Dhakksinesh, A., Olivia R. Katherine, and V. S. Pooja. "Crime Analysis and Prediction Based on Machine Learning Algorithm." In International Research Conference on IOT, Cloud and Data Science. Switzerland: Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-y21866.

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Crime prediction is a unique approach to identify and to find pattern trends of crime. Prediction means, using analysis and learning techniques, to find predictive actions of a specific activity and this is found to be effective in doing predictive analysis for various tasks such as crime prediction. The aim of this paper is to implement an approach for the problem in predicting the number of cases of crime happening in different parts of India. During the research we considered the machine learning model Random Forest and used the same for the prediction for crime. The prediction metrics used in this model are taken from feature selection technique. This technique increases the efficiency and accuracy of the prediction and also to avoid the model from over fitting. This model was tested on the crime data of India.
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Vaisband, Inna, and Eby G. Friedman. "Power Network-on-Chip for Scalable Power Delivery." In SLIP (System Level Interconnect Prediction). New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2633948.2633949.

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Zhang, Xiang, Jingwei Lu, Yang Liu, and Chung-Kuan Cheng. "Worst-Case Noise Area Prediction of On-Chip Power Distribution Network." In SLIP (System Level Interconnect Prediction). New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2633948.2633950.

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Zhou, Nancy Y., Phillip Restle, Joseph Palumbo, Joseph Kozhaya, Haifeng Qian, Zhuo Li, Charles J. Alpert, and Cliff Sze. "PACMAN." In SLIP (System Level Interconnect Prediction). New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2633948.2633951.

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Huang, Tsung-Wei, Pei-Ci Wu, and Martin D. F. Wong. "UI-Route." In SLIP (System Level Interconnect Prediction). New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2633948.2633952.

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Kemmerer, Julian, and Baris Taskin. "Range-based Dynamic Routing of Hierarchical On Chip Network Traffic." In SLIP (System Level Interconnect Prediction). New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2633948.2633953.

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Chan, Wei-Ting Jonas, Andrew B. Kahng, and Siddhartha Nath. "Methodology for Electromigration Signoff in the Presence of Adaptive Voltage Scaling." In SLIP (System Level Interconnect Prediction). New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2633948.2633954.

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Reports on the topic "Prediction"

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Manski, Charles. Interpreting the Predictions of Prediction Markets. Cambridge, MA: National Bureau of Economic Research, March 2004. http://dx.doi.org/10.3386/w10359.

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Ogunbire, Abimbola, Panick Kalambay, Hardik Gajera, and Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, December 2023. http://dx.doi.org/10.31979/mti.2023.2320.

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Nearly 5,000 people are killed and more than 418,000 are injured in weather-related traffic incidents each year. Assessments of the effectiveness of statistical models applied to crash severity prediction compared to machine learning (ML) and deep learning techniques (DL) help researchers and practitioners know what models are most effective under specific conditions. Given the class imbalance in crash data, the synthetic minority over-sampling technique for nominal (SMOTE-N) data was employed to generate synthetic samples for the minority class. The ordered logit model (OLM) and the ordered probit model (OPM) were evaluated as statistical models, while random forest (RF) and XGBoost were evaluated as ML models. For DL, multi-layer perceptron (MLP) and TabNet were evaluated. The performance of these models varied across severity levels, with property damage only (PDO) predictions performing the best and severe injury predictions performing the worst. The TabNet model performed best in predicting severe injury and PDO crashes, while RF was the most effective in predicting moderate injury crashes. However, all models struggled with severe injury classification, indicating the potential need for model refinement and exploration of other techniques. Hence, the choice of model depends on the specific application and the relative costs of false negatives and false positives. This conclusion underscores the need for further research in this area to improve the prediction accuracy of severe and moderate injury incidents, ultimately improving available data that can be used to increase road safety.
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Wolfers, Justin, and Eric Zitzewitz. Prediction Markets. Cambridge, MA: National Bureau of Economic Research, May 2004. http://dx.doi.org/10.3386/w10504.

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Rumelhart, D. E., P. G. Skokowski, and B. O. Martin. Word prediction. Office of Scientific and Technical Information (OSTI), May 1995. http://dx.doi.org/10.2172/123254.

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Cerulli, Giovanni. Non-Parametric Regression for Prediction and Scenario Analysis. Instats Inc., 2024. http://dx.doi.org/10.61700/h03w8dvg3h26b767.

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This one-day workshop, led by Giovanni Cerulli from the Research Institute on Sustainable Economic Growth, provides a comprehensive understanding of non-parametric regression for prediction and 'scenario analysis' to project the results of policies and interventions. Participants, ranging from PhD students to professional researchers across various disciplines, will gain practical skills in applying non-parametric regression using Stata, enabling them to make accurate predictions and develop scenarios in their own research.
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Buchanan, Randy, Christina Rinaudo, George Gallarno, and M. Lagarde. Early life-cycle prediction of reliability. Engineer Research and Development Center (U.S.), April 2023. http://dx.doi.org/10.21079/11681/46919.

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The intent of this project is to investigate a variety of approaches for the development of a basic model for the early life-cycle prediction of reliability (pre-Milestone A). The United States Department of Defense (DoD) currently utilizes an acquisition framework in which system development advances through a series of checkpoints known as milestones. Each milestone represents a decision point, with Milestone A being the earliest in the life cycle. At Milestone A, also known as the risk-reduction decision, the DoD evaluates design concepts while also committing funds to the maturation of technologies in an effort to mitigate future risks. Typically, little is known about the particular system to be developed at this point in the acquisition life cycle, but DoD regulations require program man-agers to submit system reliability information (OUSD[A&S] 2015). Traditional reliability predictions, however, require extensive knowledge of the system of interest to produce accurate results. This level of knowledge is unavailable at or before Milestone A, there-fore, there is a need to create models and methodologies for the prediction of system reliability. This report provides an overview of a variety of methods investigated to improve the prediction of early life cycle reliability.
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McKay, M. D. Evaluating prediction uncertainty. Office of Scientific and Technical Information (OSTI), March 1995. http://dx.doi.org/10.2172/29432.

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Henney, Carl J., Richard Radick, Donald C. Norquist, Stephen Kahler, Edward Cliver, Richard Altrock, C. N. Arge, Karatholuvu S. Balasubramaniam, and Stephen M. White. Space Weather Prediction. Fort Belvoir, VA: Defense Technical Information Center, October 2014. http://dx.doi.org/10.21236/ada612376.

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Bust, Gary S. Mesoscale Ionospheric Prediction. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada631417.

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Gates, R. K., G. J. Gibson, and K. K. McLain. Reliability Growth Prediction. Fort Belvoir, VA: Defense Technical Information Center, September 1986. http://dx.doi.org/10.21236/ada176128.

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