Academic literature on the topic 'Forecasting of data in the form of time series'
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Journal articles on the topic "Forecasting of data in the form of time series"
Angers, Jean-François, Atanu Biswas, and Raju Maiti. "Bayesian Forecasting for Time Series of Categorical Data." Journal of Forecasting 36, no. 3 (May 9, 2016): 217–29. http://dx.doi.org/10.1002/for.2426.
Full textKasyoki, Alexander. "Simple Steps for Fitting Arima Model to Time Series Data for Forecasting Using R." International Journal of Science and Research (IJSR) 4, no. 3 (April 5, 2015): 318–21. http://dx.doi.org/10.21275/sub151897.
Full textHermansah, Hermansah, Dedi Rosadi, Abdurakhman Abdurakhman, and Herni Utami. "SELECTION OF INPUT VARIABLES OF NONLINEAR AUTOREGRESSIVE NEURAL NETWORK MODEL FOR TIME SERIES DATA FORECASTING." MEDIA STATISTIKA 13, no. 2 (December 28, 2020): 116–24. http://dx.doi.org/10.14710/medstat.13.2.116-124.
Full textPhong, Pham Đinh. "A TIME SERIES FORECASTING MODEL BASED ON LINGUISTIC FORECASTING RULES." Journal of Computer Science and Cybernetics 37, no. 1 (March 29, 2021): 23–42. http://dx.doi.org/10.15625/1813-9663/37/1/15852.
Full textOrzeszko, Witold. "Several Aspects of Nonparametric Prediction of Nonlinear Time Series." Przegląd Statystyczny 65, no. 1 (January 30, 2019): 7–24. http://dx.doi.org/10.5604/01.3001.0014.0522.
Full textAlbara, Al-Khowarizmi, and Riyan Pradesyah. "Power Business Intelligence in the Data Science Visualization Process to Forecast CPO Prices." International Journal of Science, Technology & Management 2, no. 6 (November 20, 2021): 2198–208. http://dx.doi.org/10.46729/ijstm.v2i6.403.
Full textBelas, Andrii, and Petro Bidyuk. "Convolutional neural networks for modeling and forecasting nonlinear nonstationary processes." ScienceRise, no. 3 (June 30, 2021): 12–20. http://dx.doi.org/10.21303/2313-8416.2021.001924.
Full textJOSHI, BHAGAWATI P., and SANJAY KUMAR. "A COMPUTATIONAL METHOD FOR FUZZY TIME SERIES FORECASTING BASED ON DIFFERENCE PARAMETERS." International Journal of Modeling, Simulation, and Scientific Computing 04, no. 01 (December 27, 2012): 1250023. http://dx.doi.org/10.1142/s1793962312500237.
Full textRamli, Nazirah, Siti Musleha Ab Mutalib, and Daud Mohamad. "Fuzzy Time Series Forecasting Model based on Frequency Density and Similarity Measure Approach." International Journal of Engineering & Technology 7, no. 4.30 (November 30, 2018): 281. http://dx.doi.org/10.14419/ijet.v7i4.30.22284.
Full textMurat, Małgorzata, Iwona Malinowska, Magdalena Gos, and Jaromir Krzyszczak. "Forecasting daily meteorological time series using ARIMA and regression models." International Agrophysics 32, no. 2 (April 1, 2018): 253–64. http://dx.doi.org/10.1515/intag-2017-0007.
Full textDissertations / Theses on the topic "Forecasting of data in the form of time series"
Li, Jing. "Clustering and forecasting for rain attenuation time series data." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-219615.
Full textClustering is een van de unsupervised learning algorithmen om groep soortgelijke objecten in dezelfde cluster en de objecten in dezelfde cluster zijn meer vergelijkbaar met elkaar dan die in de andere clusters. Prognoser är att göra förutspårningar baserade på övergående data och effektiva artificiella intelligensmodeller för att förutspå datautveckling, som kan hjälpa till att fatta lämpliga beslut. Dataseten som används i denna avhandling är signaldämpningstidsseriedata från mikrovågsnätverket. Mikrovågsnät är kommunikationssystem för att överföra information mellan två fasta platser på jorden. De kan stödja ökade kapacitetsbehov i mobilnät och spela en viktig roll i nästa generationens trådlösa kommunikationsteknik. Men inneboende sårbarhet för slumpmässig fluktuering som nedbörd kommer att orsaka betydande nätverksförstöring. I den här avhandlingen används K-medel, Fuzzy c-medel och 2-state Hidden Markov Model för att utveckla ett steg och tvåstegs regen dämpning dataklyvningsmodeller. Prognosmodellerna är utformade utifrån k-närmaste granne-metoden och implementeras med linjär regression för att förutsäga realtidsdämpning för att hjälpa mikrovågstransportnät att mildra regnpåverkan, göra rätt beslut före tid och förbättra den allmänna prestandan.
Khadivi, Pejman. "Online Denoising Solutions for Forecasting Applications." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/72907.
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Li, Yuntao. "Federated Learning for Time Series Forecasting Using Hybrid Model." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254677.
Full textTidseriedata har blivit allmänt förekommande tack vare överkomliga kantenheter och sensorer. Mycket av denna data är värdefull för beslutsfattande. För att kunna använda datan för prognosuppgifter har den konventionella centraliserade metoden visat brister avseende storskalig datakommunikation och integritetsfrågor. Vidare har neurala nätverksmodeller inte klarat av att utnyttja den extra informationen från tidsserierna, vilket leder till misslyckanden med att ge specifikt tidsserierelaterade resultat. Båda frågorna exponerar en utmaning för storskalig tidsserieprognostisering med neurala nätverksmodeller. Alla dessa begränsningar leder till vår forskningsfråga:Kan vi realisera decentraliserad tidsserieprognostisering med en federerad lärningsmekanism som presterar jämförbart med konventionella centrala lösningar i prognostisering?I det här arbetet föreslår vi ett ramverk för federerad tidsserieprognos som löser utmaningen genom att låta användaren behålla data lokalt och lära sig en delad modell genom att aggregera lokalt beräknade uppdateringar. Dessutom utformar vi en hybrid modell för att möjliggöra neurala nätverksmodeller som kan utnyttja den extra informationen från tidsserierna för att uppnå inlärning av specifika tidsserier. Den föreslagna hybrida modellen presterar bättre än state-of-art centraliserade grundläggande modeller med NN5och Ericsson KPIdata. Samtidigt ger den federerade ansatsen jämförbara resultat med de datacentrala ansatserna för både NN5och Ericsson KPI-data. Dessa resultat svarar tillsammans på forskningsfrågan av denna avhandling.
Ben, Taieb Souhaib. "Machine learning strategies for multi-step-ahead time series forecasting." Doctoral thesis, Universite Libre de Bruxelles, 2014. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209234.
Full textHistorically, time series forecasting has been mainly studied in econometrics and statistics. In the last two decades, machine learning, a field that is concerned with the development of algorithms that can automatically learn from data, has become one of the most active areas of predictive modeling research. This success is largely due to the superior performance of machine learning prediction algorithms in many different applications as diverse as natural language processing, speech recognition and spam detection. However, there has been very little research at the intersection of time series forecasting and machine learning.
The goal of this dissertation is to narrow this gap by addressing the problem of multi-step-ahead time series forecasting from the perspective of machine learning. To that end, we propose a series of forecasting strategies based on machine learning algorithms.
Multi-step-ahead forecasts can be produced recursively by iterating a one-step-ahead model, or directly using a specific model for each horizon. As a first contribution, we conduct an in-depth study to compare recursive and direct forecasts generated with different learning algorithms for different data generating processes. More precisely, we decompose the multi-step mean squared forecast errors into the bias and variance components, and analyze their behavior over the forecast horizon for different time series lengths. The results and observations made in this study then guide us for the development of new forecasting strategies.
In particular, we find that choosing between recursive and direct forecasts is not an easy task since it involves a trade-off between bias and estimation variance that depends on many interacting factors, including the learning model, the underlying data generating process, the time series length and the forecast horizon. As a second contribution, we develop multi-stage forecasting strategies that do not treat the recursive and direct strategies as competitors, but seek to combine their best properties. More precisely, the multi-stage strategies generate recursive linear forecasts, and then adjust these forecasts by modeling the multi-step forecast residuals with direct nonlinear models at each horizon, called rectification models. We propose a first multi-stage strategy, that we called the rectify strategy, which estimates the rectification models using the nearest neighbors model. However, because recursive linear forecasts often need small adjustments with real-world time series, we also consider a second multi-stage strategy, called the boost strategy, that estimates the rectification models using gradient boosting algorithms that use so-called weak learners.
Generating multi-step forecasts using a different model at each horizon provides a large modeling flexibility. However, selecting these models independently can lead to irregularities in the forecasts that can contribute to increase the forecast variance. The problem is exacerbated with nonlinear machine learning models estimated from short time series. To address this issue, and as a third contribution, we introduce and analyze multi-horizon forecasting strategies that exploit the information contained in other horizons when learning the model for each horizon. In particular, to select the lag order and the hyperparameters of each model, multi-horizon strategies minimize forecast errors over multiple horizons rather than just the horizon of interest.
We compare all the proposed strategies with both the recursive and direct strategies. We first apply a bias and variance study, then we evaluate the different strategies using real-world time series from two past forecasting competitions. For the rectify strategy, in addition to avoiding the choice between recursive and direct forecasts, the results demonstrate that it has better, or at least has close performance to, the best of the recursive and direct forecasts in different settings. For the multi-horizon strategies, the results emphasize the decrease in variance compared to single-horizon strategies, especially with linear or weakly nonlinear data generating processes. Overall, we found that the accuracy of multi-step-ahead forecasts based on machine learning algorithms can be significantly improved if an appropriate forecasting strategy is used to select the model parameters and to generate the forecasts.
Lastly, as a fourth contribution, we have participated in the Load Forecasting track of the Global Energy Forecasting Competition 2012. The competition involved a hierarchical load forecasting problem where we were required to backcast and forecast hourly loads for a US utility with twenty geographical zones. Our team, TinTin, ranked fifth out of 105 participating teams, and we have been awarded an IEEE Power & Energy Society award.
Doctorat en sciences, Spécialisation Informatique
info:eu-repo/semantics/nonPublished
Mercurio, Danilo. "Adaptive estimation for financial time series." Doctoral thesis, [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=972597263.
Full textWinn, David. "An analysis of neural networks and time series techniques for demand forecasting." Thesis, Rhodes University, 2007. http://hdl.handle.net/10962/d1004362.
Full textMarriott, Richard Keyworth. "Estimating and forecasting a demand chain for food using cross-section and time-series data." Thesis, University of Bristol, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266903.
Full textDíaz, González Fernando. "Federated Learning for Time Series Forecasting Using LSTM Networks: Exploiting Similarities Through Clustering." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254665.
Full textFederated Learning utgör en statistisk utmaning vid träning med starkt heterogen sekvensdata. Till exempel så uppvisar tidsseriedata inom telekomdomänen blandade variationer och mönster över längre tidsintervall. Dessa distinkta fördelningar utgör en utmaning när en nod inte bara ska bidra till skapandet av en global modell utan även ämnar applicera denna modell på sin lokala datamängd. Att i detta scenario införa en global modell som ska passa alla kan visa sig vara otillräckligt, även om vi använder oss av de mest framgångsrika modellerna inom maskininlärning för tidsserieprognoser, Long Short-Term Memory (LSTM) nätverk, vilka visat sig kunna fånga komplexa mönster och generalisera väl till nya mönster. I detta arbete visar vi att genom att klustra klienterna med hjälp av dessa mönster och selektivt aggregera deras uppdateringar i olika globala modeller kan vi uppnå förbättringar av den lokal prestandan med minimala kostnader, vilket vi demonstrerar genom experiment med riktigt tidsseriedata och en grundläggande LSTM-modell.
Li, Lei. "Fast Algorithms for Mining Co-evolving Time Series." Research Showcase @ CMU, 2011. http://repository.cmu.edu/dissertations/112.
Full textKruger, Albertus Stephanus. "An investigation into the use of combined linear and neural network models for time series data / A.S. Kruger." Thesis, North-West University, 2009. http://hdl.handle.net/10394/4782.
Full textThesis (M.Sc. (Computer Science))--North-West University, Vaal Triangle Campus, 2010.
Books on the topic "Forecasting of data in the form of time series"
A, Dickey David, ed. SAS for forecasting time series. 2nd ed. New York: J. Wiley, 2003.
Find full textBrocklebank, John Clare. SAS system for forecasting time series. Cary, NC: SAS Institute, 1986.
Find full textMaiti, Raju. Modelling and coherent forecasting of zero-inflated time series count data. Ahmedabad: Indian Institute of Management, 2013.
Find full textRiazuddin, Riaz. Detection and forecasting of Islamic calendar effects in Time Series Data. Karachi: State Bank of Pakistan, 2002.
Find full textLiu, Lon-Mu. Forecasting and time series analysis using the SCA statistical system. River Forest, Ill: Scientific Computing Associates, 2004.
Find full textBastian, Jörgen. Optimale Zeitreihenprognose: Empirische Probleme und Lösungen. Frankfurt am Main: P. Lang, 1985.
Find full textBagus, Erich. Computergestützte Zeitreihenprognose mit linear-rekursiven Modellen. Idstein: Schulz-Kirchner Verlag, 1994.
Find full textIntelligent systems and financial forecasting. London: Springer, 1997.
Find full textDickey, David A., and John C. Brocklebank. SAS for Forecasting Time Series. WA (Wiley-SAS), 2003.
Find full textAgung, I. Gusti Ngurah. Advanced Time Series Data Analysis: Forecasting Using EViews. Wiley & Sons, Incorporated, John, 2018.
Find full textBook chapters on the topic "Forecasting of data in the form of time series"
Devi, J. Vimala, and K. S. Kavitha. "Time Series Analysis and Forecasting on Crime Data." In Algorithms for Intelligent Systems, 281–97. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6332-1_26.
Full textArratia, Argimiro, and Eduardo Sepúlveda. "Convolutional Neural Networks, Image Recognition and Financial Time Series Forecasting." In Mining Data for Financial Applications, 60–69. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37720-5_5.
Full textZufferey, Thierry, Andreas Ulbig, Stephan Koch, and Gabriela Hug. "Forecasting of Smart Meter Time Series Based on Neural Networks." In Data Analytics for Renewable Energy Integration, 10–21. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50947-1_2.
Full textCarriegos, Miguel V., and Ramón Ángel Fernández-Díaz. "Towards Forecasting Time-Series of Cyber-Security Data Aggregates." In 13th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2020), 273–81. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57805-3_26.
Full textCubadda, Gianluca, and Barbara Guardabascio. "On the Use of PLS Regression for Forecasting Large Sets of Cointegrated Time Series." In Advanced Statistical Methods for the Analysis of Large Data-Sets, 171–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21037-2_16.
Full textMomin, Bashirahamad, and Gaurav Chavan. "Univariate Time Series Models for Forecasting Stationary and Non-stationary Data: A Brief Review." In Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 2, 219–26. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63645-0_24.
Full textBarbaglia, Luca, Sergio Consoli, and Sebastiano Manzan. "Exploring the Predictive Power of News and Neural Machine Learning Models for Economic Forecasting." In Mining Data for Financial Applications, 135–49. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66981-2_11.
Full textArratia, Argimiro, Gustavo Avalos, Alejandra Cabaña, Ariel Duarte-López, and Martí Renedo-Mirambell. "Sentiment Analysis of Financial News: Mechanics and Statistics." In Data Science for Economics and Finance, 195–216. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66891-4_9.
Full textNayak, Sarat Chandra, Bijan Bihari Misra, and Satchidananda Dehuri. "Hybridization of the Higher Order Neural Networks with the Evolutionary Optimization Algorithms—An Application to Financial Time Series Forecasting." In Advances in Machine Learning for Big Data Analysis, 119–44. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8930-7_5.
Full textBasellini, Ugofilippo, and Carlo Giovanni Camarda. "A Three-Component Approach to Model and Forecast Age-at-Death Distributions." In Developments in Demographic Forecasting, 105–29. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42472-5_6.
Full textConference papers on the topic "Forecasting of data in the form of time series"
Iosevich, S., G. Arutyunyants, and Z. Hou. "Dynamic aggregation for time series forecasting." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363996.
Full textKalinggo, Bonnie Alexandra, and Zulkarnain. "Time Series Forecasting for Non-stationary Data." In APCORISE 2020: 3rd Asia Pacific Conference on Research in Industrial and Systems Engineering 2020. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3400934.3400952.
Full textAlmuammar, Manal, and Maria Fasli. "Deep Learning for Non-stationary Multivariate Time Series Forecasting." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006192.
Full textHorelu, Adriana, Catalin Leordeanu, Elena Apostol, Dan Huru, Mariana Mocanu, and Valentin Cristea. "Forecasting Techniques for Time Series from Sensor Data." In 2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2015. http://dx.doi.org/10.1109/synasc.2015.49.
Full textWu, Wenrui, Tao Tao, Jing Shang, Ding Xiao, Chuan Shi, and Yong Jiang. "Sequence Attention for Multivariate Time Series Forecasting." In 2021 IEEE Sixth International Conference on Data Science in Cyberspace (DSC). IEEE, 2021. http://dx.doi.org/10.1109/dsc53577.2021.00019.
Full textAbbas, Zainab, Jon Reginbald Ivarsson, Ahmad Al-Shishtawy, and Vladimir Vlassov. "Scaling Deep Learning Models for Large Spatial Time-Series Forecasting." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9005475.
Full textLi, Zhaoxi, Jun He, Hongyan Liu, and Xiaoyong Du. "Combining Global and Sequential Patterns for Multivariate Time Series Forecasting." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378380.
Full textFerreira, Tiago A. E., Germano C. Vasconcelos, and Paulo J. L. Adeodato. "A New Evolutionary Approach for Time Series Forecasting." In 2007 IEEE Symposium on Computational Intelligence and Data Mining. IEEE, 2007. http://dx.doi.org/10.1109/cidm.2007.368933.
Full textMuntean, Maria Viorela, and Daniela Onita. "Agent for Preprocessing and Forecasting Time-Series Data." In 2018 10th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE, 2018. http://dx.doi.org/10.1109/ecai.2018.8679048.
Full textFeng, Cong, Erol Kevin Chartan, Bri-Mathias Hodge, and Jie Zhang. "Characterizing Time Series Data Diversity for Wind Forecasting." In UCC '17: 10th International Conference on Utility and Cloud Computing. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3148055.3148065.
Full textReports on the topic "Forecasting of data in the form of time series"
Mikayilov, Jeyhun, Ryan Alyamani, Abdulelah Darandary, Muhammad Javid, Fakhri Hasanov, Saleh T. AlTurki, and Rey B. Arnaiz. Modeling and Forecasting Industrial Electricity Demand for Saudi Arabia: Uncovering Regional Characteristics. King Abdullah Petroleum Studies and Research Center, January 2022. http://dx.doi.org/10.30573/ks--2021-dp19.
Full textMikayilov, Jeyhun, Ryan Alyamani, Abdulelah Darandary, Muhammad Javid, and Fakhri Hasanov. Modeling and Forecasting Industrial Electricity Demand for Saudi Arabia: Uncovering Regional Characteristics. King Abdullah Petroleum Studies and Research Center, January 2022. http://dx.doi.org/10.30573/ks--2021-dp22.
Full textRussell, H. A. J., and S. K. Frey. Canada One Water: integrated groundwater-surface-water-climate modelling for climate change adaptation. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/329092.
Full textSemerikov, Serhiy, Illia Teplytskyi, Yuliia Yechkalo, Oksana Markova, Vladimir Soloviev, and Arnold Kiv. Computer Simulation of Neural Networks Using Spreadsheets: Dr. Anderson, Welcome Back. [б. в.], June 2019. http://dx.doi.org/10.31812/123456789/3178.
Full textBendikov, Michael, and Thomas C. Harmon. Development of Agricultural Sensors Based on Conductive Polymers. United States Department of Agriculture, August 2006. http://dx.doi.org/10.32747/2006.7591738.bard.
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