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Relevant bibliographies by topics / Autoregressive Conditional Heteroskedasticity (ARCH)

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Academic literature on the topic 'Autoregressive Conditional Heteroskedasticity (ARCH)'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Autoregressive Conditional Heteroskedasticity (ARCH)"

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Jati, Kumara. "ANALISIS EFEK MUSIM HUJAN DAN KEMARAU TERHADAP HARGA BERAS." Jurnal Manajemen Industri dan Logistik 2, no. 1 (2018): 40–51. http://dx.doi.org/10.30988/jmil.v2i1.24.

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This study analyzes the effects of the rainy and dry seasons on rice prices. Autoregressive and Moving Average (ARMA) and Autoregressive Conditional Heteroskedasticity / Generalized Autoregressive Conditional Heteroskedasticity (ARCH / GARCH) with a dummy variable. We used daily data of the stock and the price of rice from January 29, 2014 until January 29, 2018. ARMA (0,1)-ARCH (1) model with dummy variable that is dry season is more influence conditional variance of rice price compared with rainy season dummy variable. Stakeholders need to pay more attention to fluctuations in rice prices, e

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Wang, W., P. H. A. J. M. Van Gelder, J. K. Vrijling, and J. Ma. "Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes." Nonlinear Processes in Geophysics 12, no. 1 (2005): 55–66. http://dx.doi.org/10.5194/npg-12-55-2005.

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Abstract. Conventional streamflow models operate under the assumption of constant variance or season-dependent variances (e.g. ARMA (AutoRegressive Moving Average) models for deseasonalized streamflow series and PARMA (Periodic AutoRegressive Moving Average) models for seasonal streamflow series). However, with McLeod-Li test and Engle's Lagrange Multiplier test, clear evidences are found for the existence of autoregressive conditional heteroskedasticity (i.e. the ARCH (AutoRegressive Conditional Heteroskedasticity) effect), a nonlinear phenomenon of the variance behaviour, in the residual ser

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Kuziboev, Bekhzod, Petra Vysušilová, Raufhon Salahodjaev, Alibek Rajabov, and Tukhtabek Rakhimov. "The Volatility Assessment of CO2 Emissions in Uzbekistan: ARCH/GARCH Models." International Journal of Energy Economics and Policy 13, no. 5 (2023): 1–7. http://dx.doi.org/10.32479/ijeep.14487.

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The study is pioneer to investigate the volatility of CO2 emissions in Uzbekistan. To this end, ARCH (Autoregressive Conditional Heteroskedasticity) and GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models are used spanning the period 1925-2021 for the annual data of CO2 emissions. The results indicate that ARCH model is more adequate that GARCH model in the volatility assessment. Furthermore, it is found that the volatility of CO2 emissions in Uzbekistan is very high. The policymakers have to consider the high volatility of CO2 emissions in the environmental policy measure

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Diebold, Francis X., Steve C. Lim, and C. Jevons Lee. "A Note on Conditional Heteroskedasticity in the Market Model." Journal of Accounting, Auditing & Finance 8, no. 2 (1993): 141–50. http://dx.doi.org/10.1177/0148558x9300800203.

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We examine the usefulness and implications of modeling conditional heteroskedasticity in market model residual returns. Autoregressive conditional heteroskedasticity (ARCH) plays a key role in our approach. To highlight the salient issues, we first provide a case study of one firm, Winn-Dixie Stores. Formal testing procedures reveal strong ARCH effects. ARCH models are then estimated and used to infer the pattern of time-varying volatility; differences in parameter estimates caused by use of the fully efficient estimator are also noted. Next, we provide a systematic examination of the entire N

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Jati (Kementerian Perdagangan), Kumara. "ANALISIS EFEK MUSIM HUJAN DAN KEMARAU TERHADAP HARGA BERAS." JURNAL MANAJEMEN INDUSTRI DAN LOGISTIK 2, no. 1 (2018): 37. http://dx.doi.org/10.30988/jmil.v2i1.68.

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<p><em>Penelitian ini menganalisis efek dari musim hujan dan kemarau terhadap harga beras. Metode yang digunakan yaitu Autoregressive and Moving Average (ARMA) dan Autoregressive Conditional Heteroskedasticity/Generalized Autoregressive Conditional Heteroskedasticity (ARCH/GARCH) dengan variabel dummy. Data yang digunakan yaitu stok dan harga beras harian dari 29 Januari 2014 sampai dengan 29 Januari 2018. Penggunaan model ARMA-ARCH/GARCH dapat menunjukkan bahwa model ini bisa untuk membantu melihat pola pergerakan harga beras. Model ARMA (0,1)-ARCH (1) dengan variabel dummy yaitu

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Halim, Siana, Shirley Adelia, and Jani Rahardjo. "MODEL MATEMATIK UNTUK MENENTUKAN NILAI TUKAR MATA UANG RUPIAH TERHADAP DOLLAR AMERIKA." Jurnal Teknik Industri 1, no. 1 (2004): 30–40. http://dx.doi.org/10.9744/jti.1.1.30-40.

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The main objective of this paper is to estimate parameters in the heteroskedasticity models, particularly in Auto Regressive Conditional Heteroskedasticity - ARCH(1) and Generalized Autoregressive Conditional Heteroskedasticity- GARCH(1,1). These models will be used to fit, to forecast and to update the volatility of Rupiah Vs US.Dollar rate.  In order to get the estimation of fitting and updating parameters of ARCH(1) and GARCH(1,1), here will be used iterative method which is derived from the standard maximum likelihood estimation and the initial values are taken from the result of Yul

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Venkateswara Rao, K., D. Srilatha, D. Jagan Mohan Reddy, Venkata Subbaiah Desanamukula, and Mandefro Legesse Kejela. "Regression Based Price Prediction of Staple Food Materials Using Multivariate Models." Scientific Programming 2022 (June 13, 2022): 1–7. http://dx.doi.org/10.1155/2022/4572064.

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Profit margins for essential foodstuffs could be a demand rising problem. There are several variables influencing currency fluctuations. For example, the various variables of commodity food prices are climate, crude prices, and so on. Forecasting the fluctuating prices of basic foodstuffs is also relevant even for the government, producers, and customers. The article will use ARCH (autoregressive conditional heteroskedasticity) to forecast the essential food market considering external conditions. The findings agree well enough with the assessment price in the industry by employing two main ap

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Sulistiowati, Dwi, Maya Sari Syahrul, and Iswan Rina. "Pemodelan Harga Saham Menggunakan Arma-Garch." Jurnal Penelitian Dan Pengkajian Ilmiah Eksakta 1, no. 2 (2022): 89–93. http://dx.doi.org/10.47233/jppie.v1i2.532.

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Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models were used for modeling with heteroscedasticity data. This study aims to determine the time series model on the stock price data of PT Triputra Agro Persada Tbk. (TAPG) with modeling ARMA, ARCH and GARCH. Based on the smallest Akaike Information Criterion (AIC) and Schwarz Criterion (SC), it shows that the ARMA(1,0)-GARCH(2,1) model is the best model for predicting the value of TAPG stock prices.

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Hokayem, Jihad El, Joseph Gemayel, and Dany Mezher. "Forecasting Oil Prices: A Comparative Study." International Journal of Economics and Finance 14, no. 7 (2022): 55. http://dx.doi.org/10.5539/ijef.v14n7p55.

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Oil prices have been a major concern for many policy makers, businesses and individuals throughout the years. The spillover of inflation, which is at its highest level since several decades, due to the supply chain problems and spike in energy prices, following the war between Russia and Ukraine pushed oil and gas under the spotlight recognizing its crucial role. In turn, this has imposed many challenges on numerous countries across regions building up feelings of fear and anxiety amid serious concerns about energy and food security. Forecasting oil prices is still a major challenge as it is s

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Dwi Murniati, Ni Luh Ketut, Indwiarti Indwiarti, and Aniq Atiqi Rohmawati. "Implemetasi Model Autoregressive (AR) Dan Autoregressive Conditional Heteroskedasticity (ARCH) Untuk Memprediksi Harga Emas." Indonesian Journal on Computing (Indo-JC) 3, no. 2 (2018): 29. http://dx.doi.org/10.21108/indojc.2018.3.2.225.

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Gold is a one of high selling value items in the market, and it can be used as an investment item. The price of gold in the market tends to be stable and not undergoing too significant changes which makes gold be a very valuable item. The aim of this research is to predict gold price using AR (1) and ARCH (1) model which are the part of time series methods. The data of gold price is obtained from ANTAM's daily historical website from 2007 - 2017. Here, the basic information about data is given by using descriptive statistic and the estimation of parameters in each model is condacted by using &

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Dissertations / Theses on the topic "Autoregressive Conditional Heteroskedasticity (ARCH)"

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Stenberg, Erik. "On the Autoregressive Conditional Heteroskedasticity Models." Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-295175.

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Chang, Tsangyao. "An Application of Autoregressive Conditional Heteroskedasticity (Arch) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Modelling on Taiwan's Time-Series Data: Three Essays." DigitalCommons@USU, 1995. http://digitalcommons.usu.edu/etd/4040.

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In this dissertation, three essays are presented that apply recent advances in time-series methods to the analysis of inflation and stock market index data for Taiwan. Specifically, ARCH and GARCH methodologies are used to investigate claims of increased volatility in economic time-series data since 1980. In the first essay, analysis that accounts for structural change reveals that the fundamental relationship between inflation and its variability was severed by policies implemented during economic liberalization in Taiwan in the early 1980s. Furthermore, if residuals are corrected for serial

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Silvennoinen, Annastiina. "Essays on autoregressive conditional heteroskedasticity." Doctoral thesis, Stockholm : Economic Research Institute, Stockholm School of Economics (EFI), 2006. http://www2.hhs.se/EFI/summary/711.htm.

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Katsiampa, Paraskevi. "Nonlinear exponential autoregressive time series models with conditional heteroskedastic errors with applications to economics and finance." Thesis, Loughborough University, 2015. https://dspace.lboro.ac.uk/2134/18432.

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The analysis of time series has long been the subject of interest in different fields. For decades time series were analysed with linear models, which have many advantages. Nevertheless, an issue which has been raised is whether there exist other models that can explain and forecast real data better than linear ones. In this thesis, new nonlinear time series models are suggested, which consist of a nonlinear conditional mean model, such as an ExpAR or an Extended ExpAR, and a nonlinear conditional variance model, such as an ARCH or a GARCH. Since new models are introduced, simulated series of

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Van, Heerden Petrus Marthinus Stephanus. "The relationship between the forward– and the realized spot exchange rate in South Africa / Petrus Marthinus Stephanus van Heerden." Thesis, North-West University, 2010. http://hdl.handle.net/10394/4511.

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The inability to effectively hedge against unfavourable exchange rate movements, using the current forward exchange rate as the only guideline, is a key inhibiting factor of international trade. Market participants use the current forward exchange rate quoted in the market to make decisions regarding future exchange rate changes. However, the current forward exchange rate is not solely determined by the interaction of demand and supply, but is also a mechanistic estimation, which is based on the current spot exchange rate and the carry cost of the transaction. Results of various studies, inclu

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Fernandes, Ana Margarida Gonçalves de Sousa. "Construção de um Índice Sintético para o Mercado Accionista Português: 1977 - 2007." Master's thesis, Instituto Superior de Economia e Gestão, 2009. http://hdl.handle.net/10400.5/1472.

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Mestrado em Finanças<br>O objectivo deste trabalho consiste na reconstrução da série histórica do índice PSI20, cuja cotação se iniciou em 31 de Dezembro de 1992, utilizando para o facto, as observações diárias disponíveis, do já extinto índice BT&A, no período compreendido entre 1 de Abril de 1977 e 30 de Dezembro de 1992. Para tal, recorreu-se à metodologia subjacente aos principais modelos teóricos de heterocedasticidade condicionada que, de acordo com diversos estudos empíricos desenvolvidos, demonstraram uma forte adequabilidade quando aplicados ao índice PSI20. Uma vez seleccionado o mod

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Bovo, Vitor Juliano. "Volatility Triggered Range Forward (VTRF): an instrument for protection against volatility fluctuations in the BRL/USD pair." reponame:Repositório Institucional do FGV, 2011. http://hdl.handle.net/10438/8552.

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Submitted by Vitor Bovo (vitorbovo@hotmail.com) on 2011-08-26T00:07:04Z No. of bitstreams: 1 Dissertação Vitor Bovo - Final Protocolada.pdf: 2961673 bytes, checksum: 2da3f793ce6283d4140f390c02baee53 (MD5)<br>Approved for entry into archive by Gisele Isaura Hannickel (gisele.hannickel@fgv.br) on 2011-08-26T15:37:19Z (GMT) No. of bitstreams: 1 Dissertação Vitor Bovo - Final Protocolada.pdf: 2961673 bytes, checksum: 2da3f793ce6283d4140f390c02baee53 (MD5)<br>Approved for entry into archive by Gisele Isaura Hannickel (gisele.hannickel@fgv.br) on 2011-08-26T15:37:32Z (GMT) No. of bitstreams: 1 D

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Osuntuyi, Ayokunle Anthony <1980&gt. "Essays on Bayesian inference with financial applications." Doctoral thesis, Università Ca' Foscari Venezia, 2014. http://hdl.handle.net/10579/4605.

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This thesis is composed of two main research lines. The first line, developed in chapters 2 to 4, deals with frequentist and Bayesian estimation of regime-switching GARCH models and its application to risk management on energy markets, while the second part, which corresponds to chapter 5, focuses on forecast rationality testing within a Bayesian framework. Chapter 2 presents a unified mathematical framework for characterizing the class of MS-GARCH models based on collapsing the regimes in order to eliminate the usual path dependence problem. Within this framework, two new models (identified a

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Royer, Julien. "Processus ARCH d'ordre infini, Bêtas dynamiques et applications financières." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAG012.

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La modélisation des séries temporelles financières est rendue difficile par la présence de faits stylisés. Ces propriétés statistiques empiriques rendent nécessaires l'utilisation de modèles non-linéaires hétéroscédastiques. Les modèles ARCH d'ordre infini ont été introduits afin de permettre une modélisation plus fine de ces faits stylisés, et en particulier le phénomène de forte persistance des chocs de volatilités. Nous présentons de nouvelles extensions à ces modèles flexibles et étudions leur inférence. En premier lieu, nous considérons un modèle ARCH d'ordre infini asymétrique. Nous démo

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Arotiba, Gbenga Joseph. "Pricing American Style Employee Stock Options having GARCH Effects." Thesis, University of the Western Cape, 2010. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_3057_1298615964.

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<p>We investigate some simulation-based approaches for the valuing of the employee stock options. The mathematical models that deal with valuation of such options include the work of Jennergren and Naeslund [L.P Jennergren and B. Naeslund, A comment on valuation of executive stock options and the FASB proposal, Accounting Review 68 (1993) 179-183]. They used the Black and Scholes [F. Black and M. Scholes, The pricing of options and corporate liabilities, Journal of Political Economy 81(1973) 637-659] and extended partial differential equation for an option that includes the early exercise. Som

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Books on the topic "Autoregressive Conditional Heteroskedasticity (ARCH)"

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Biekpe, Nicholas. Bilinear generalised autoregressive conditional heteroskedasticity: With applications to the equity market. Queen's University, 1993.

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Bashir, A. A review of autoregressive conditional heteroscedastic (arch)times series models. UMIST, 1994.

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Hurn, A. Stan. The empirical size and power of some tests for detecting autoregressive conditional heteroskedasticity in the presenceof serial correlation. Glasgow University, Department of Political Economy, 1995.

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Shi, Feng. Learn About the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Model in R With Data From the DJIA 30 Stock Time Series (2018). SAGE Publications Ltd., 2019. http://dx.doi.org/10.4135/9781526487650.

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Book chapters on the topic "Autoregressive Conditional Heteroskedasticity (ARCH)"

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Hassler, Uwe. "Processes with Autoregressive Conditional Heteroskedasticity (ARCH)." In Stochastic Processes and Calculus. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23428-1_6.

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Kirchgässner, Gebhard, and Jürgen Wolters. "Autoregressive Conditional Heteroskedasticity." In Introduction to Modern Time Series Analysis. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73291-4_7.

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Chen, Jenny K. "Generalized AutoRegressive Conditional Heteroskedasticity Model." In Financial Data Analytics with R. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003469704-8.

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Teräsvirta, Timo. "Nonlinear Models for Autoregressive Conditional Heteroskedasticity." In Handbook of Volatility Models and Their Applications. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118272039.ch2.

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Flach, Leonardo, Realdo de Oliveira, Jonatas Dutra Sallaberry, and Luísa Karam de Mattos. "Blockchain and New Digital Technologies: Explaining the Bitcoin Volatility with a Generalized Autoregressive Conditional Heteroskedasticity Model." In Digital Technologies and Transformation in Business, Industry and Organizations. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07626-8_8.

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Kumar, Sanjay, Meenakshi Srivastava, and Vijay Prakash. "Forecasting Mutual Fund Volatility Using Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Model: Evidence from the Indian Mutual Fund." In Recent Advances in Computational Intelligence and Cyber Security. CRC Press, 2024. http://dx.doi.org/10.1201/9781003518587-7.

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Bollerslev, Tim. "Generalized Autoregressive Conditional Heteroskedasticity." In Arch. Oxford University PressOxford, 1995. http://dx.doi.org/10.1093/oso/9780198774310.003.0003.

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Abstract A natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in Engle (1982) to allow for past conditional variances in the current conditional variance equation is proposed. Stationarity conditions and autocorrelation structure for this new class of parametric models are derived. Maximum likelihood estimation and testing are also considered. Finally an empirical example relating to the uncertainty of the inflation rate is presented. While conventional time-series and econometric models operate under an assumption of constant variance, the ARCH

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"21.1. Autoregressive Conditional Heteroskedasticity (ARCH)." In Time Series Analysis. Princeton University Press, 1994. http://dx.doi.org/10.1515/9780691218632-181.

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Bollerslev, Tim. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model." In Arch. Oxford University PressOxford, 1995. http://dx.doi.org/10.1093/oso/9780198774310.003.0014.

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Abstract A multivariate time-series model with time-varying conditional variances and covariances, but constant conditional correlations is proposed. In a multivariate regression framework, the model is readily interpreted as an extension of the Seemingly Unrelated Regression (SUR) model allowing for heteroskedasticity. Parameterizing each of the conditional variances as a univariate Generalized Autoregressive Conditional Heteroskedastic (GARCH) process, the descriptive validity of the model is illustrated for a set of five nominal European US dollar exchange rates following the inception of t

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Attri, Shradha, Sanjeev Gupta, and Sachin Singh. "Risk Forecasting Using Artificial Intelligence and Machine Learning." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-1200-2.ch009.

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The financial market is where physical or virtual assets like foreign exchange, stock, cryptocurrency, and derivatives can be bought and sold. The study examined the role of artificial intelligence and machine learning techniques, mainly focusing on the stock and cryptocurrency markets, which represent physical and virtual assets. Due to the high volatility in the stock and cryptocurrency market, traditional statistical tools like Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) families, Autoregressive Integrated Movin

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Conference papers on the topic "Autoregressive Conditional Heteroskedasticity (ARCH)"

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Staugaitis, Algirdas Justinas. "Financial speculation impact on agricultural commodity price volatility: TGARCH approach." In 21st International Scientific Conference "Economic Science for Rural Development 2020". Latvia University of Life Sciences and Technologies. Faculty of Economics and Social Development, 2020. http://dx.doi.org/10.22616/esrd.2020.53.014.

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Motivated by agricultural commodity price fluctuations and spikes in the last decade, we investigate whether financial speculation destabilizes the price of agricultural commodities. The aim of this research is to assess the impact of financial speculation on agricultural commodity price volatility. In our study we use weekly returns on wheat, soybean and corn futures from Chicago Mercantile of Exchange. To measure this impact, we apply autoregressive conditional heteroskedasticity (ARCH) technique. We also propose a model with seasonal dummy variables to measure if financial speculation impac

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Ou, ChengQi, Charlene Xie, Jun Xu, and YunLiang Hu. "Generalized Autoregressive Conditional Heteroskedasticity in Credit Risk Measurement." In 2009 International Conference on Management and Service Science (MASS). IEEE, 2009. http://dx.doi.org/10.1109/icmss.2009.5304395.

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Chen, Yuejian, Ke Feng, Robert B. Randall, Pietro Borghesani, and Ming Jian Zuo. "Use of Autoregressive Conditional Heteroskedasticity Model to Assess Gear Tooth Surface Roughness." In 2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM). IEEE, 2020. http://dx.doi.org/10.1109/aparm49247.2020.9209389.

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Ranjan, Nikhil, Hema A. Murthy, and Timothy A. Gonsalves. "Detection of SYN flooding attacks using generalized autoregressive conditional heteroskedasticity (GARCH) modeling technique." In 2010 National Conference On Communications (NCC). IEEE, 2010. http://dx.doi.org/10.1109/ncc.2010.5430151.

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Wang, Y., M. Sznaier, O. Camps, and F. Pait. "Identification of a class of generalized autoregressive conditional heteroskedasticity (GARCH) models with applications to covariance propagation." In 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, 2015. http://dx.doi.org/10.1109/cdc.2015.7402327.

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Forain, Igor, Adilson E. Guelfi, Elvis Pontes, and Anderson Silva. "Detecção de Intrusão Utilizando Análise de Séries Temporais com Modelos ARMAX/GARCH." In Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais. Sociedade Brasileira de Computação - SBC, 2013. http://dx.doi.org/10.5753/sbseg.2013.19539.

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O objetivo deste trabalho é propor um método de detecção de intrusão por anomalia no tráfego de pacotes de rede aplicando modelos autoregressivos de média móvel com entradas exógenas (Autoregressive Moving Average Exogenous - ARMAX) e autorregressivos com heteroscedasticidade condicional (Generalized Autoregressive Conditional Heteroskedasticity – GARCH). Em termos experimentais, utilizando as bases de tráfego (dataset) disponibilizadas pela DARPA (1999), durante a análise de ataques de negação de serviço synflood e comportamentos de varredura de redes executados por meio de pacotes TCP SYN, o

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Aida, Liza Nur, Swasono Rahardjo, and Vita Kusumasari. "Generalized space time autoregressive integrated autoregressive conditional heteroscedastic (GSTARI-ARCH) modeling with least squares and MLE parameter estimation." In THE 4TH INTERNATIONAL CONFERENCE ON MATHEMATICS AND ITS APPLICATIONS (ICOMATHAPP) 2023: Mathematics and its Applications on Society 5.0: Challenges and Opportunities. AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0235733.

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Sheng, Hu, and YangQuan Chen. "The Modeling of Great Salt Lake Elevation Time Series Based on ARFIMA With Stable Innovations." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86864.

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Great Salt Lake (GSL) is the largest salt lake in the western hemisphere, the fourth-largest terminal lake in the world. The elevation of Great Salt Lake has critical effect on the people who live nearby and their properties. It is crucial to build an exact model of GSL elevation time series in order to predict the GSL elevation precisely. Although some models, such as FARIMA or ARFIMA (Auto-Regressive Fractional Integral and Moving Average), GARCH (Generalized Auto-Regressive Conditional Heteroskedasticity) and FIGARCH (Fractional Integral Generalized Auto-Regressive Conditional Heteroskedast

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Goyal, Vipul, Mengyu Xu, Jayanta Kapat, and Ladislav Vesely. "Prediction Enhancement of Machine Learning Using Time Series Modeling in Gas Turbines." In ASME Turbo Expo 2021: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/gt2021-59082.

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Abstract Blade-path temperature can serve as a precursor of anomalies in combustion system and/or cooling system. Given observations from blade-path temperature sensors of a power plant, we consider prediction of the temperature for each sensor. The only extraneous predictor is the combustion turbine fuel flow, while measurements of other potential predictors are unavailable. Long-memory behavior and heterogeneous variance are observed from the residuals of the generalized additive model. Autoregressive Fractionally Integrated Moving Average (ARFIMA) and Generalized Autoregressive Conditional

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Silva, Lucas Barth, Roberto Zanetti Freire, and Osíris Canciglieri Junior. "Spot Energy Price Forecasting Using Wavelet Transform and Extreme Learning Machine." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-62.

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Given the social importance of energy, there is a concern to promote the sustainable development of the sector. Aiming at this evolution, from the 90s onwards, a wave of liberalization in the sector began to emerge in various parts of the world. These measures promoted an increase in the dynamism of commercial transactions and the transformation of electricity into a commodity. Consequently, futures, short-term, and spot markets were created. In this context, and due to the volatility of energy prices, the forecast of monetary values has become strategic for traders. This work aims to apply a

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