Relevant bibliographies by topics / K-Nearest Neighbor - Time Series Prediction

Contents

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

Academic literature on the topic 'K-Nearest Neighbor - Time Series Prediction'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'K-Nearest Neighbor - Time Series Prediction.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "K-Nearest Neighbor - Time Series Prediction"

1

Deng, Mingjun, and Shiru Qu. "Road Short-Term Travel Time Prediction Method Based on Flow Spatial Distribution and the Relations." Mathematical Problems in Engineering 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/7626875.

Full text
Abstract:
There are many short-term road travel time forecasting studies based on time series, but indeed, road travel time not only relies on the historical travel time series, but also depends on the road and its adjacent sections history flow. However, few studies have considered that. This paper is based on the correlation of flow spatial distribution and the road travel time series, applying nearest neighbor and nonparametric regression method to build a forecasting model. In aspect of spatial nearest neighbor search, three different space distances are defined. In addition, two forecasting functions are introduced: one combines the forecasting value by mean weight and the other uses the reciprocal of nearest neighbors distance as combined weight. Three different distances are applied in nearest neighbor search, which apply to the two forecasting functions. For travel time series, the nearest neighbor and nonparametric regression are applied too. Then minimizing forecast error variance is utilized as an objective to establish the combination model. The empirical results show that the combination model can improve the forecast performance obviously. Besides, the experimental results of the evaluation for the computational complexity show that the proposed method can satisfy the real-time requirement.
APA, Harvard, Vancouver, ISO, and other styles
2

She, Dunxian, and Xiaohua Yang. "A New Adaptive Local Linear Prediction Method and Its Application in Hydrological Time Series." Mathematical Problems in Engineering 2010 (2010): 1–15. http://dx.doi.org/10.1155/2010/205438.

Full text
Abstract:
The embedding dimension and the number of nearest neighbors are very important parameters in the prediction of a chaotic time series. In order to reduce the uncertainties in the determination of the forgoing two parameters, a new adaptive local linear prediction method is proposed in this study. In the new method, the embedding dimension and the number of nearest neighbors are combined as a parameter set and change adaptively in the process of prediction. The generalized degree of freedom is used to help select the optimal parameters. Real hydrological time series are taken to examine the performance of the new method. The prediction results indicate that the new method can choose the optimal parameters of embedding dimension and the nearest neighbor number adaptively in the prediction process. And the nonlinear hydrological time series perhaps could be modeled better by the new method.
APA, Harvard, Vancouver, ISO, and other styles
3

Priambodo, Bagus, and Yuwan Jumaryadi. "Time Series Traffic Speed Prediction Using k-Nearest Neighbour Based on Similar Traffic Data." MATEC Web of Conferences 218 (2018): 03021. http://dx.doi.org/10.1051/matecconf/201821803021.

Full text
Abstract:
During the past few years, time series models and neural network models are widely used to predict traffic flow and traffic congestion based on historical data. Historical data traffic from sensors is often applied to time series prediction or various neural network predictions. Recent research shows that traffic flow pattern will be different on weekdays and weekends. We conducted a time series prediction of traffic flow on Monday, using data on weekdays and whole days data. Prediction of short time traffic flows on Monday based on weekdays data using k-NN methods shows a better result, compared to prediction based on all day’s data. We compared the results of the experiment using k-NN and Neural Network methods. From this study, we observed that generally, using similar traffic data for time series prediction show a better result than using the whole data.
APA, Harvard, Vancouver, ISO, and other styles
4

Tang, Li, Ping He Pan, and Yong Yi Yao. "EPAK: A Computational Intelligence Model for 2-level Prediction of Stock Indices." International Journal of Computers Communications & Control 13, no. 2 (April 13, 2018): 268–79. http://dx.doi.org/10.15837/ijccc.2018.2.3187.

Full text
Abstract:
This paper proposes a new computational intelligence model for predicting univariate time series, called EPAK, and a complex prediction model for stock market index synthesizing all the sector index predictions using EPAK as a kernel. The EPAK model uses a complex nonlinear feature extraction procedure integrating a forward rolling Empirical Mode Decomposition (EMD) for financial time series signal analysis and Principal Component Analysis (PCA) for dimension reduction to generate information-rich features as input to a new two-layer K-Nearest Neighbor (KNN) with Affinity Propagation (AP) clustering for prediction via regression. The EPAK model is then used as a kernel for predicting each of all the sector indices of the stock market. The sector indices predictions are then synthesized via weighted average to generate the prediction of the stock market index, yielding a complex prediction model for the stock market index. The EPAK model and the complex prediction model for stock index are tested on real historical financial time series in Chinese stock index including CSI 300 and ten sector indices, with results confirming the effectiveness of the proposed models.
APA, Harvard, Vancouver, ISO, and other styles
5

Koutroumbas, K., and A. Belehaki. "One-step ahead prediction of <i>fo</i>F2 using time series forecasting techniques." Annales Geophysicae 23, no. 9 (November 22, 2005): 3035–42. http://dx.doi.org/10.5194/angeo-23-3035-2005.

Full text
Abstract:
Abstract. In this paper the problem of one-step ahead prediction of the critical frequency (foF2) of the middle-latitude ionosphere, using time series forecasting methods, is considered. The whole study is based on a sample of about 58000 observations of foF2 with 15-min time resolution, derived from the Athens digisonde ionograms taken from the Digisonde Portable Sounder (DPS4) located at Palaia Penteli (38° N, 23.5° E), for the period from October 2002 to May 2004. First, the embedding dimension of the dynamical system that generates the above sample is estimated using the false nearest neighbor method. This information is then utilized for the training of the predictors employed in this study, which are the linear predictor, the neural network predictor, the persistence predictor and the k-nearest neighbor predictor. The results obtained by the above predictors suggest that, as far as the mean square error is considered as performance criterion, the first two predictors are significantly better than the latter two predictors. In addition, the results obtained by the linear and the neural network predictors are not significantly different from each other. This may be taken as an indication that a linear model suffices for one step ahead prediction of foF2.
APA, Harvard, Vancouver, ISO, and other styles
6

Miśkiewicz-Nawrocka, Monika. "The Application of Random Noise Reduction By Nearest Neighbor Method To Forecasting of Economic Time Series." Folia Oeconomica Stetinensia 13, no. 2 (July 8, 2014): 96–108. http://dx.doi.org/10.2478/foli-2013-0020.

Full text
Abstract:
Abstract Since the deterministic chaos appeared in the literature, we have observed a huge increase in interest in nonlinear dynamic systems theory among researchers, which has led to the creation of new methods of time series prediction, e.g. the largest Lyapunov exponent method and the nearest neighbor method. Real time series are usually disturbed by random noise, which can complicate the problem of forecasting of time series. Since the presence of noise in the data can significantly affect the quality of forecasts, the aim of the paper will be to evaluate the accuracy of predicting the time series filtered using the nearest neighbor method. The test will be conducted on the basis of selected financial time series.
APA, Harvard, Vancouver, ISO, and other styles
7

Cortez, Klender, Martha del Pilar Rodríguez-García, and Samuel Mongrut. "Exchange Market Liquidity Prediction with the K-Nearest Neighbor Approach: Crypto vs. Fiat Currencies." Mathematics 9, no. 1 (December 29, 2020): 56. http://dx.doi.org/10.3390/math9010056.

Full text
Abstract:
In this paper, we compare the predictions on the market liquidity in crypto and fiat currencies between two traditional time series methods, the autoregressive moving average (ARMA) and the generalized autoregressive conditional heteroskedasticity (GARCH), and the machine learning algorithm called the k-nearest neighbor (KNN) approach. We measure market liquidity as the log rates of bid-ask spreads in a sample of three cryptocurrencies (Bitcoin, Ethereum, and Ripple) and 16 major fiat currencies from 9 February 2018 to 8 February 2019. We find that the KNN approach is better suited for capturing the market liquidity in a cryptocurrency in the short-term than the ARMA and GARCH models maybe due to the complexity of the microstructure of the market. Considering traditional time series models, we find that ARMA models perform well when estimating the liquidity of fiat currencies in developed markets, whereas GARCH models do the same for fiat currencies in emerging markets. Nevertheless, our results show that the KNN approach can better predict the log rates of the bid-ask spreads of crypto and fiat currencies than ARMA and GARCH models.
APA, Harvard, Vancouver, ISO, and other styles
8

Su, Liyun, and Chenlong Li. "Local Functional Coefficient Autoregressive Model for Multistep Prediction of Chaotic Time Series." Discrete Dynamics in Nature and Society 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/329487.

Full text
Abstract:
A new methodology, which combines nonparametric method based on local functional coefficient autoregressive (LFAR) form with chaos theory and regional method, is proposed for multistep prediction of chaotic time series. The objective of this research study is to improve the performance of long-term forecasting of chaotic time series. To obtain the prediction values of chaotic time series, three steps are involved. Firstly, the original time series is reconstructed inm-dimensional phase space with a time delayτby using chaos theory. Secondly, select the nearest neighbor points by using local method in them-dimensional phase space. Thirdly, we use the nearest neighbor points to get a LFAR model. The proposed model’s parameters are selected by modified generalized cross validation (GCV) criterion. Both simulated data (Lorenz and Mackey-Glass systems) and real data (Sunspot time series) are used to illustrate the performance of the proposed methodology. By detailed investigation and comparing our results with published researches, we find that the LFAR model can effectively fit nonlinear characteristics of chaotic time series by using simple structure and has excellent performance for multistep forecasting.
APA, Harvard, Vancouver, ISO, and other styles
9

BOLLT, ERIK M. "MODEL SELECTION, CONFIDENCE AND SCALING IN PREDICTING CHAOTIC TIME-SERIES." International Journal of Bifurcation and Chaos 10, no. 06 (June 2000): 1407–22. http://dx.doi.org/10.1142/s0218127400000906.

Full text
Abstract:
Assuming a good embedding and additive noise, the traditional approach to time-series embedding prediction has been to predict pointwise by (usually linear) regression of the k-nearest neighbors; no good mathematics has been previously developed to appropriately select the model (where to truncate Taylor's series) to balance the conflict between noise fluctuations of a small k, and large k data needs of fitting many parameters of a high ordered model. We present a systematic approach to: (1) select the statistically significant neighborhood for a fixed (usually linear) model, (2) give an unbiased estimate of predicted mean response together with a statement of quality of the prediction in terms of confidence bands.
APA, Harvard, Vancouver, ISO, and other styles
10

WAYLAND, R., D. PICKETT, D. BROMLEY, and A. PASSAMANTE. "MEASURING THE PREDICTABILITY OF NOISY RECURRENT TIME SERIES." International Journal of Bifurcation and Chaos 03, no. 03 (June 1993): 797–802. http://dx.doi.org/10.1142/s0218127493000738.

Full text
Abstract:
The effect of the chosen forecasting method on the measured predictability of a noisy recurrent time series is investigated. Situations where the length of the time series is limited, and where the level of corrupting noise is significant are emphasized. Two simple prediction methods based on explicit nearest-neighbor averages are compared to a more complicated, and computationally expensive, local linearization technique based on the method of total least squares. The comparison is made first for noise-free, and then for noisy time series. It is shown that when working with short time series in high levels of additive noise, the simple prediction schemes perform just as well as the more sophisticated total least squares method.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "K-Nearest Neighbor - Time Series Prediction"

1

Aikes, Junior Jorge. "Estudo da influência de diversas medidas de similaridade na previsão de séries temporais utilizando o algoritmo KNN-TSP." Universidade Estadual do Oeste do Parana, 2012. http://tede.unioeste.br:8080/tede/handle/tede/1084.

Full text
Abstract:
Made available in DSpace on 2017-07-10T17:11:50Z (GMT). No. of bitstreams: 1 JORGE AIKES JUNIOR.PDF: 2050278 bytes, checksum: f5bae18bbcb7465240488c45b2c813e7 (MD5) Previous issue date: 2012-04-11
Time series can be understood as any set of observations which are time ordered. Among the many possible tasks appliable to temporal data, one that has attracted increasing interest, due to its various applications, is the time series forecasting. The k-Nearest Neighbor - Time Series Prediction (kNN-TSP) algorithm is a non-parametric method for forecasting time series. One of its advantages, is its easiness application when compared to parametric methods. Even though its easier to define kNN-TSP s parameters, some issues remain opened. This research is focused on the study of one of these parameters: the similarity measure. This parameter was empirically evaluated using various similarity measures in a large set of time series, including artificial series with seasonal and chaotic characteristics, and several real world time series. It was also carried out a case study comparing the predictive accuracy of the kNN-TSP algorithm with the Moving Average (MA), univariate Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and multivariate SARIMA methods in a time series of a Korean s hospital daily patients flow in the Emergency Department. This work also proposes an approach to the development of a hybrid similarity measure which combines characteristics from several measures. The research s result demonstrated that the Lp Norm s measures have an advantage over other measures evaluated, due to its lower computational cost and for providing, in general, greater accuracy in temporal data forecasting using the kNN-TSP algorithm. Although the literature in general adopts the Euclidean similarity measure to calculate de similarity between time series, the Manhattan s distance can be considered an interesting candidate for defining similarity, due to the absence of statistical significant difference and to its lower computational cost when compared to the Euclidian measure. The measure proposed in this work does not show significant results, but it is promising for further research. Regarding the case study, the kNN-TSP algorithm with only the similarity measure parameter optimized achieves a considerably lower error than the MA s best configuration, and a slightly greater error than the univariate e multivariate SARIMA s optimal settings presenting less than one percent of difference.
Séries temporais podem ser entendidas como qualquer conjunto de observações que se encontram ordenadas no tempo. Dentre as várias tarefas possíveis com dados temporais, uma que tem atraído crescente interesse, devido a suas várias aplicações, é a previsão de séries temporais. O algoritmo k-Nearest Neighbor - Time Series Prediction (kNN-TSP) é um método não-paramétrico de previsão de séries temporais que apresenta como uma de suas vantagens a facilidade de aplicação, quando comparado aos métodos paramétricos. Apesar da maior facilidade na determinação de seus parâmetros, algumas questões relacionadas continuam em aberto. Este trabalho está focado no estudo de um desses parâmetros: a medida de similaridade. Esse parâmetro foi avaliado empiricamente utilizando diversas medidas de similaridade em um grande conjunto de séries temporais que incluem séries artificiais, com características sazonais e caóticas, e várias séries reais. Foi realizado também um estudo de caso comparativo entre a precisão da previsão do algoritmo kNN-TSP e a dos métodos de Médias Móveis (MA), Auto-regressivos de Médias Móveis Integrados Sazonais (SARIMA) univariado e SARIMA multivariado, em uma série de fluxo diário de pacientes na Área de Emergência de um hospital coreano. Neste trabalho é ainda proposta uma abordagem para o desenvolvimento de uma medida de similaridade híbrida, que combine características de várias medidas. Os resultados obtidos neste trabalho demonstram que as medidas da Norma Lp apresentam vantagem sobre as demais medidas avaliadas, devido ao seu menor custo computacional e por apresentar, em geral, maior precisão na previsão de dados temporais utilizando o algoritmo kNN-TSP. Apesar de na literatura, em geral, a medida Euclidiana ser adotada como medida de similaridade, a medida Manhattan pode ser considerada candidata interessante para definir a similaridade entre séries temporais, devido a não apresentar diferença estatisticamente significativa com a medida Euclidiana e possuir menor custo computacional. A medida proposta neste trabalho, não apresenta resultados significantes, mas apresenta-se promissora para novas pesquisas. Com relação ao estudo de caso, o algoritmo kNN-TSP, com apenas o parâmetro de medida de similaridade otimizado, alcança um erro consideravelmente inferior a melhor configuração com MA, e pouco maior que as melhores configurações dos métodos SARIMA univariado e SARIMA multivariado, sendo essa diferença inferior a um por cento.
APA, Harvard, Vancouver, ISO, and other styles
2

Lantz, Robin. "Time series monitoring and prediction of data deviations in a manufacturing industry." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-100181.

Full text
Abstract:
An automated manufacturing industry makes use of many interacting moving parts and sensors. Data from these sensors generate complex multidimensional data in the production environment. This data is difficult to interpret and also difficult to find patterns in. This project provides tools to get a deeper understanding of Swedsafe’s production data, a company involved in an automated manufacturing business. The project is based on and will show the potential of the multidimensional production data. The project mainly consists of predicting deviations from predefined threshold values in Swedsafe’s production data. Machine learning is a good method of finding relationships in complex datasets. Supervised machine learning classification is used to predict deviation from threshold values in the data. An investigation is conducted to identify the classifier that performs best on Swedsafe's production data. The technique sliding window is used for managing time series data, which is used in this project. Apart from predicting deviations, this project also includes an implementation of live graphs to easily get an overview of the production data. A steady production with stable process values is important. So being able to monitor and predict events in the production environment can provide the same benefit for other manufacturing companies and is therefore suitable not only for Swedsafe. The best performing machine learning classifier tested in this project was the Random Forest classifier. The Multilayer Perceptron did not perform well on Swedsafe’s data, but further investigation in recurrent neural networks using LSTM neurons would be recommended. During the projekt a web based application displaying the sensor data in live graphs is also developed.
APA, Harvard, Vancouver, ISO, and other styles
3

Ferrero, Carlos Andres. "Algoritmo kNN para previsão de dados temporais: funções de previsão e critérios de seleção de vizinhos próximos aplicados a variáveis ambientais em limnologia." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-19052009-135128/.

Full text
Abstract:
A análise de dados contendo informações sequenciais é um problema de crescente interesse devido à grande quantidade de informação que é gerada, entre outros, em processos de monitoramento. As séries temporais são um dos tipos mais comuns de dados sequenciais e consistem em observações ao longo do tempo. O algoritmo k-Nearest Neighbor - Time Series Prediction kNN-TSP é um método de previsão de dados temporais. A principal vantagem do algoritmo é a sua simplicidade, e a sua aplicabilidade na análise de séries temporais não-lineares e na previsão de comportamentos sazonais. Entretanto, ainda que ele frequentemente encontre as melhores previsões para séries temporais parcialmente periódicas, várias questões relacionadas com a determinação de seus parâmetros continuam em aberto. Este trabalho, foca-se em dois desses parâmetros, relacionados com a seleção de vizinhos mais próximos e a função de previsão. Para isso, é proposta uma abordagem simples para selecionar vizinhos mais próximos que considera a similaridade e a distância temporal de modo a selecionar os padrões mais similares e mais recentes. Também é proposta uma função de previsão que tem a propriedade de manter bom desempenho na presença de padrões em níveis diferentes da série temporal. Esses parâmetros foram avaliados empiricamente utilizando várias séries temporais, inclusive caóticas, bem como séries temporais reais referentes a variáveis ambientais do reservatório de Itaipu, disponibilizadas pela Itaipu Binacional. Três variáveis limnológicas fortemente correlacionadas são consideradas nos experimentos de previsão: temperatura da água, temperatura do ar e oxigênio dissolvido. Uma análise de correlação é realizada para verificar se os dados previstos mantem a correlação das variáveis. Os resultados mostram que, o critério de seleção de vizinhos próximos e a função de previsão, propostos neste trabalho, são promissores
Treating data that contains sequential information is an important problem that arises during the data mining process. Time series constitute a popular class of sequential data, where records are indexed by time. The k-Nearest Neighbor - Time Series Prediction kNN-TSP method is an approximator for time series prediction problems. The main advantage of this approximator is its simplicity, and is often used in nonlinear time series analysis for prediction of seasonal time series. Although kNN-TSP often finds the best fit for nearly periodic time series forecasting, some problems related to how to determine its parameters still remain. In this work, we focus in two of these parameters: the determination of the nearest neighbours and the prediction function. To this end, we propose a simple approach to select the nearest neighbours, where time is indirectly taken into account by the similarity measure, and a prediction function which is not disturbed in the presence of patterns at different levels of the time series. Both parameters were empirically evaluated on several artificial time series, including chaotic time series, as well as on a real time series related to several environmental variables from the Itaipu reservoir, made available by Itaipu Binacional. Three of the most correlated limnological variables were considered in the experiments carried out on the real time series: water temperature, air temperature and dissolved oxygen. Analyses of correlation were also accomplished to verify if the predicted variables values maintain similar correlation as the original ones. Results show that both proposals, the one related to the determination of the nearest neighbours as well as the one related to the prediction function, are promising
APA, Harvard, Vancouver, ISO, and other styles
4

Pathirana, Vindya Kumari. "Nearest Neighbor Foreign Exchange Rate Forecasting with Mahalanobis Distance." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5757.

Full text
Abstract:
Foreign exchange (FX) rate forecasting has been a challenging area of study in the past. Various linear and nonlinear methods have been used to forecast FX rates. As the currency data are nonlinear and highly correlated, forecasting through nonlinear dynamical systems is becoming more relevant. The nearest neighbor (NN) algorithm is one of the most commonly used nonlinear pattern recognition and forecasting methods that outperforms the available linear forecasting methods for the high frequency foreign exchange data. The basic idea behind the NN is to capture the local behavior of the data by selecting the instances having similar dynamic behavior. The most relevant k number of histories to the present dynamical structure are the only past values used to predict the future. Due to this reason, NN algorithm is also known as the k-nearest neighbor algorithm (k-NN). Here k represents the number of chosen neighbors. In the k-nearest neighbor forecasting procedure, similar instances are captured through a distance function. Since the forecasts completely depend on the chosen nearest neighbors, the distance plays a key role in the k-NN algorithm. By choosing an appropriate distance, we can improve the performance of the algorithm significantly. The most commonly used distance for k-NN forecasting in the past was the Euclidean distance. Due to possible correlation among vectors at different time frames, distances based on deterministic vectors, such as Euclidean, are not very appropriate when applying for foreign exchange data. Since Mahalanobis distance captures the correlations, we suggest using this distance in the selection of neighbors. In the present study, we used five different foreign currencies, which are among the most traded currencies, to compare the performances of the k-NN algorithm with traditional Euclidean and Absolute distances to performances with the proposed Mahalanobis distance. The performances were compared in two ways: (i) forecast accuracy and (ii) transforming their forecasts in to a more effective technical trading rule. The results were obtained with real FX trading data, and the results showed that the method introduced in this work outperforms the other popular methods. Furthermore, we conducted a thorough investigation of optimal parameter choice with different distance measures. We adopted the concept of distance based weighting to the NN and compared the performances with traditional unweighted NN algorithm based forecasting. Time series forecasting methods, such as Auto regressive integrated moving average process (ARIMA), are widely used in many ares of time series as a forecasting technique. We compared the performances of proposed Mahalanobis distance based k-NN forecasting procedure with the traditional general ARIM- based forecasting algorithm. In this case the forecasts were also transformed into a technical trading strategy to create buy and sell signals. The two methods were evaluated for their forecasting accuracy and trading performances. Multi-step ahead forecasting is an important aspect of time series forecasting. Even though many researchers claim that the k-Nearest Neighbor forecasting procedure outperforms the linear forecasting methods for financial time series data, and the available work in the literature supports this claim with one step ahead forecasting. One of our goals in this work was to improve FX trading with multi-step ahead forecasting. A popular multi-step ahead forecasting strategy was adopted in our work to obtain more than one day ahead forecasts. We performed a comparative study on the performance of single step ahead trading strategy and multi-step ahead trading strategy by using five foreign currency data with Mahalanobis distance based k-nearest neighbor algorithm.
APA, Harvard, Vancouver, ISO, and other styles
5

Raykhel, Ilya Igorevitch. "Real-Time Automatic Price Prediction for eBay Online Trading." BYU ScholarsArchive, 2008. https://scholarsarchive.byu.edu/etd/1631.

Full text
Abstract:
While Machine Learning is one of the most popular research areas in Computer Science, there are still only a few deployed applications intended for use by the general public. We have developed an exemplary application that can be directly applied to eBay trading. Our system predicts how much an item would sell for on eBay based on that item's attributes. We ran our experiments on the eBay laptop category, with prior trades used as training data. The system implements a feature-weighted k-Nearest Neighbor algorithm, using genetic algorithms to determine feature weights. Our results demonstrate an average prediction error of 16%; we have also shown that this application greatly reduces the time a reseller would need to spend on trading activities, since the bulk of market research is now done automatically with the help of the learned model.
APA, Harvard, Vancouver, ISO, and other styles
6

Hsu, Ching-Hsiang, and 許景翔. "Hybrid K-nearest neighbor and Time Series to Detect and Predict DDoS Attack in SDN." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/b2432y.

Full text
Abstract:
碩士
國立交通大學
資訊管理研究所
106
Due to DDoS attacks, it easily makes system consume large resources to response malicious host and system cannot provide normal service. Besides, this condition also happens in the Software Defined Network architecture. When malicious host launch DDoS to the SDN, it makes more serious consequences than legacy network. The core of DDoS is the central controller and it manages the whole network. When it is attacked, the overall system will be crushed. Therefore, controller is the weakness in the SDN. In order to prevent DDoS attacks from malicious host, this paper implements the prediction module and protection module to reduce the resource waste. The main method has two part. First, use time series to prediction how many packets will be receive according to the history data. Second, use KNN to cluster which packets is normal or abnormal. Last, we combine these parameters and calculate the probability of host be attacked in the future. By our method, we can detect and predict the future attack and do more prevention behavior to add these malicious hosts in the blacklist to reduce resource consume.
APA, Harvard, Vancouver, ISO, and other styles
7

Merkwirth, Christian. "Nächste-Nachbar basierte Methoden in der nichtlinearen Zeitreihenanalyse." 2000. http://hdl.handle.net/11858/00-1735-0000-0006-B40F-A.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Wang, Jhi-Lian, and 王智聯. "Predicting Chaotic Time Series using a Self-Tuned ANN with Nearest-Neighbor Sets Trained by an OLL Algorithm." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/97257374795561063427.

Full text
Abstract:
碩士
國立中正大學
化學工程研究所
85
Chaotic predictions are carried out for a chaotic Lorenz time seriersby four different methods:(I)A local predictor, (II) A global ANN with an accelerated alorithm for multilayer preceptron - an OLL(Optimization Layerby Layer) learning scheme, (III)A hybrid neural network - A global ANN withthe nearest neighbors as the training datas with an OLL learning scheme. (IV)A self-tuned ANN with the nearest neighbors and an OLL learning scheme.Then, we use three prediction techniques: a.New value prediction - predict next step without updataing the data. b.Superior prediction - prediction nextstep with updataing data. c.Prediction from fixed point - we predict next step from a fixed point with every different data pair. Results indicate that prediction accuracy for the method(III) is extremelygood. Its relative error below 1e-8, but we can't obtain any benefit from IV.We have attempted many kinds of network structures for predicting chaotic time series, and we know the different results are obtained for network, the method whose structure changed from simple to complicate ones. It seem to tell us that we can find the optimal network structure for the studied system, but we must use the other method for reduce the number of weights. The prediction accuracy and the predict horizon of three method are b>a>c.
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "K-Nearest Neighbor - Time Series Prediction"

1

Sorjamaa, Antti, Jin Hao, and Amaury Lendasse. "Mutual Information and k-Nearest Neighbors Approximator for Time Series Prediction." In Lecture Notes in Computer Science, 553–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11550907_87.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Yakowitz, S., and M. Karlsson. "Nearest Neighbor Methods for Time Series, with Application to Rainfall/Runoff Prediction." In Advances in the Statistical Sciences: Stochastic Hydrology, 149–60. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-4792-4_9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

De La Vega, Erick, Juan J. Flores, and Mario Graff. "k-Nearest-Neighbor by Differential Evolution for Time Series Forecasting." In Nature-Inspired Computation and Machine Learning, 50–60. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13650-9_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Abu Bakar, Azuraliza, Almahdi Mohammed Ahmed, and Abdul Razak Hamdan. "Discretization of Time Series Dataset Using Relative Frequency and K-Nearest Neighbor Approach." In Advanced Data Mining and Applications, 193–201. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17316-5_18.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Giao, Bui Cong, and Duong Tuan Anh. "Efficient k-Nearest Neighbor Search for Static Queries over High Speed Time-Series Streams." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 83–97. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15392-6_9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Lahmiri, Salim. "Practical Machine Learning in Financial Market Trend Prediction." In Advances in Business Information Systems and Analytics, 206–17. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-5958-2.ch010.

Full text
Abstract:
Using the wavelet analysis for low-frequency time series extraction, the authors in this chapter conduct out-of-sample predictions of the S&P500 price index future trend (up and down) following two trading strategies. In particular, the goal is to separately predict an increase or decrease of stock market by 0.5%. Indeed, predicting market increases by 0.5% is suitable to active portfolio managers, whilst predicting its decreases by 0.5% is suitable to risk-averse portfolio managers to limit losses. The Support Vector Machine (SVM) with polynomial kernel is used as the baseline forecasting model. Its performance is respectively compared to that of the Probabilistic Neural Networks (PNN) and the well known k-Nearest Neighbour (k-NN) algorithm, which is a statistical classifier. The simulation results reveal that the predictive system based on the SVM with wavelet analysis coefficients as inputs outperforms all the other systems. The achieved accuracy is 98.13%. As a result, it is concluded that the wavelet transform and SVM as an integrated system are appropriate to capture the S&P500 price changes by more or less than 0.5%.
APA, Harvard, Vancouver, ISO, and other styles
7

Shang, Yingying. "LAR: A User Behavior Prediction Model in Server Log Based on LSTM-Attention Network and RSC Algorithm." In Fuzzy Systems and Data Mining VI. IOS Press, 2020. http://dx.doi.org/10.3233/faia200709.

Full text
Abstract:
Using server log data to predict the URLs that a user is likely to visit is an important research area in user behavior prediction. In this paper, a predictive model (called LAR) based on the long short-term memory (LSTM) attention network and reciprocal-nearest-neighbors supported clustering algorithm (RSC) for predicting the URL is proposed. First, the LSTM-attention network is used to predict the URL categories a user might visit, and the RSC algorithm is then used to cluster users. Subsequently, the URLs belonging to the same category are determined from the user clusters to predict the URLs that the user might visit. The proposed LAR model considers the time sequence of the user access URL, and the relationship between a single user and group users, which effectively improves the prediction accuracy. The experimental results demonstrate that the LAR model is feasible and effective for user behavior prediction. The accuracy of the mean absolute error and root mean square error of the LAR model are better than those of the other models compared in this study.
APA, Harvard, Vancouver, ISO, and other styles
8

Doshi, Aditya Ashvin, Prabu Sevugan, and P. Swarnalatha. "Modified Support Vector Machine Algorithm to Reduce Misclassification and Optimizing Time Complexity." In Big Data Analytics for Satellite Image Processing and Remote Sensing, 34–56. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3643-7.ch003.

Full text
Abstract:
A number of methodologies are available in the field of data mining, machine learning, and pattern recognition for solving classification problems. In past few years, retrieval and extraction of information from a large amount of data is growing rapidly. Classification is nothing but a stepwise process of prediction of responses using some existing data. Some of the existing prediction algorithms are support vector machine and k-nearest neighbor. But there is always some drawback of each algorithm depending upon the type of data. To reduce misclassification, a new methodology of support vector machine is introduced. Instead of having the hyperplane exactly in middle, the position of hyperplane is to be change per number of data points of class available near the hyperplane. To optimize the time consumption for computation of classification algorithm, some multi-core architecture is used to compute more than one independent module simultaneously. All this results in reduction in misclassification and faster computation of class for data point.
APA, Harvard, Vancouver, ISO, and other styles
9

Ramanujam, Elangovan, L. Rasikannan, S. Viswa, and B. Deepan Prashanth. "Predictive Strength of Ensemble Machine Learning Algorithms for the Diagnosis of Large Scale Medical Datasets." In Applications of Big Data in Large- and Small-Scale Systems, 260–81. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-6673-2.ch016.

Full text
Abstract:
Machine learning is not a simple technology but an amazing field having more and more to explore. It has a number of real-time applications such as weather forecast, price prediction, gaming, medicine, fraud detection, etc. Machine learning has an increased usage in today's technological world as data is growing in volumes and machine learning is capable of producing mathematical and statistical models that can analyze complex data and generate accurate results. To analyze the scalable performance of the learning algorithms, this chapter utilizes various medical datasets from the UCI Machine Learning repository ranges from smaller to large datasets. The performance of learning algorithms such as naïve Bayes, decision tree, k-nearest neighbor, and stacking ensemble learning method are compared in different evaluation models using metrics such as accuracy, sensitivity, specificity, precision, and f-measure.
APA, Harvard, Vancouver, ISO, and other styles
10

Singh, Aman, and Babita Pandey. "An Efficient Diagnosis System for Detection of Liver Disease Using a Novel Integrated Method Based on Principal Component Analysis and K-Nearest Neighbor (PCA-KNN)." In Intelligent Systems, 1015–30. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5643-5.ch042.

Full text
Abstract:
Talk about organ failure and people immediately recall kidney diseases. On the contrary, there is no such alertness about liver diseases and its failure despite the fact that this disease is one of the leading causes of mortality worldwide. Therefore, an effective diagnosis and in time treatment of patients is paramount. This study accordingly aims to construct an intelligent diagnosis system which integrates principle component analysis (PCA) and k-nearest neighbor (KNN) methods to examine the liver patient dataset. The model works with the combination of feature extraction and classification performed by PCA and KNN respectively. Prediction results of the proposed system are compared using statistical parameters that include accuracy, sensitivity, specificity, positive predictive value and negative predictive value. In addition to higher accuracy rates, the model also attained remarkable sensitivity and specificity, which were a challenging task given an uneven variance among attribute values in the dataset.
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "K-Nearest Neighbor - Time Series Prediction"

1

Tang, Li, Heping Pan, and Yiyong Yao. "K-Nearest Neighbor Regression with Principal Component Analysis for Financial Time Series Prediction." In the 2018 International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3194452.3194467.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Shi, Aiguo, and Bo Zhou. "K-nearest neighbor LS-SVM method for multi-step prediction of chaotic time series." In 2012 IEEE Symposium on Electrical & Electronics Engineering (EEESYM). IEEE, 2012. http://dx.doi.org/10.1109/eeesym.2012.6258677.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Filali Boubrahimi, Soukaina, and Rafal Angryk. "Multivariate Time Series Nearest Neighbor Search: A Case Study on Solar Flare Prediction." In 2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE). IEEE, 2018. http://dx.doi.org/10.1109/aike.2018.00035.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Fan, Zhaoya, Jichao Chen, Tao Zhang, Ning Shi, and Wei Zhang. "Machine Learning for Formation Tightness Prediction and Mobility Prediction." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/206208-ms.

Full text
Abstract:
Abstract From the perspective of wireline formation test (WFT), formation tightness reflects the "speed" of pressure buildup while the pressure test is being conducted. We usually define a pressure test point that has a very low pressure-buildup speed as a tight point. The mobility derived from this kind of pressure point is usually less than 0.01md/cP; otherwise, the pressure points will be defined as valid points with valid formation pressure and mobility. Formation tightness reflects the formation permeability information and can be an indicator to estimate the difficulty of the WFT pumping and sampling operation. Mobility, as compared to permeability, reflects the dynamic supply capacity of the formation. A rapid and good mobility prediction based on petrophysical logging can not only directly provide valid formation productivity but can also evaluate the feasibility of the WFT and doing optimization work in advance. Compared to a time-consuming and costly drillstem test (DST) operation, the WFT is the most efficient and cost-saving method to confirm hydrocarbon presence. However, the success rate of WFT sampling operations in the deep Kuqa formation is less than 50% overall, mostly due to the formation tightness exceeding the capability of the tools. Therefore, a rapid mobility evaluation is necessary to meet WFT feasibility analysis. As companion work to a previous WFT optimization study(SPE-195932-MS), we further studied and discuss the machine learning for mobility prediction. In the previous study, we formed a mobility prediction workflow by doing a statistical analysis of more than 1000 pressure test points with several statistical mathematic methods, such as univariate linear regression (ULR), multivariate linear regression (MLR), neural network regression analysis (NNA), and decision tree classification analysis (DTA) methods. In this paper, the methods and principles of machine learning are expounded. A series of machine learning methods were tested. The algorithms that are appropriate for these specific data set were selected. Includes DTA, discriminant analysis (DA), logistic regression, support vector machine (SVM), K-nearest neighbor (KNN) for formation tightness prediction and linear regression, DTA, SVM, Gaussian process regression SVM, random tree, neural network analysis for mobility prediction. Contrastive analysis reveals that: The SVM classifier has the best result over other methods for formation tightness probability prediction. Based on R squared and RMSE analysis, linear regression, GPR, and NNA delivered relatively good results compared with other mobility prediction methods. An optimized data processing workflow was proposed, and it delivered a better result than the workflow proposed in SPE-195932-MS under the same training and testing dataset condition. The comparison between measured mobility and predicted mobility results reveals that, in most situations, the predicted mobility and measured mobility matched very well with each other. WFT were conducted in newly drilled wells. Sampling success rate also achieved 100% in these wells by optimizing the WFT tool string and sampling stations selection in advance, and NPT is significantly reduced.
APA, Harvard, Vancouver, ISO, and other styles
5

Patil, Akshata, and Sanchita Jha. "Real - time download prediction based on the k - nearest neighbor method." In 2011 Second Asian Himalayas International Conference on Internet (AH-ICI). IEEE, 2011. http://dx.doi.org/10.1109/ahici.2011.6113929.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Liu, Tao, Jihui Ma, Wei Guan, Yue Song, and Hu Niu. "Bus Arrival Time Prediction Based on the k-Nearest Neighbor Method." In 2012 Fifth International Joint Conference on Computational Sciences and Optimization (CSO). IEEE, 2012. http://dx.doi.org/10.1109/cso.2012.111.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Lv, Yisheng, Shuming Tang, and Hongxia Zhao. "Real-Time Highway Traffic Accident Prediction Based on the k-Nearest Neighbor Method." In 2009 International Conference on Measuring Technology and Mechatronics Automation. IEEE, 2009. http://dx.doi.org/10.1109/icmtma.2009.657.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Ahmed, Almahdi Mohammed, Azuraliza Abu Bakar, and Abdul Razak Hamdan. "Improved SAX time series data representation based on Relative Frequency and K-Nearest Neighbor Algorithm." In 2010 10th International Conference on Intelligent Systems Design and Applications (ISDA). IEEE, 2010. http://dx.doi.org/10.1109/isda.2010.5687092.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Tak, Sehyun, Sunghoon Kim, Kiate Jang, and Hwasoo Yeo. "Real-Time Travel Time Prediction Using Multi-Level k-Nearest Neighbor Algorithm and Data Fusion Method." In 2014 International Conference on Computing in Civil and Building Engineering. Reston, VA: American Society of Civil Engineers, 2014. http://dx.doi.org/10.1061/9780784413616.231.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Sambasivan, Lokesh Kumar, Venkataramana Bantwal Kini, Srikanth Ryali, Joydeb Mukherjee, and Dinkar Mylaraswamy. "Comparison of a Few Fault Diagnosis Methods on Sparse Variable Length Time Series Sequences." In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-27843.

Full text
Abstract:
Accurate gas turbine engine Fault Detection and Diagnosis (FDD) is essential to improving aircraft safety as well as in reducing airline costs associated with delays and cancellations. This paper compares broadly three methods of fault detection and diagnosis (FDD) dealing with variable length time sequences. Chosen methods are based on Dynamic Time Warping (DTW), k-Nearest Neighbor method, Hidden Markov Model (HMM) and a Support Vector Machine (SVM) which makes use of DTW ingeniously as its kernel. The time sequences are obtained from Turbo Propulsion Engines in their nominal conditions and two faulty conditions. Typically there is paucity of faulty exemplars and the challenge is to come up with algorithms which work reasonably well under such circumstances. Also, normalization of data plays a significant role in determining the performance of the classifiers used for FDD in terms of their detection rate and false positives. In particular spherical normalization has been explored considering the advantage of its superior normalization properties. Given sparse training data how well each of these algorithms performs is shown by means of tests performed on time series data collected at normal and faulty modes from a turbofan gas turbine propulsion engine and the results are presented.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'K-Nearest Neighbor - Time Series Prediction' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography