Relevant bibliographies by topics / ARIMA Modeling

Contents

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

Academic literature on the topic 'ARIMA Modeling'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 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 'ARIMA Modeling.'

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 "ARIMA Modeling"

1

Panjaitan, Helmi, Alan Prahutama, and Sudarno Sudarno. "PERAMALAN JUMLAH PENUMPANG KERETA API MENGGUNAKAN METODE ARIMA, INTERVENSI DAN ARFIMA (Studi Kasus : Penumpang Kereta Api Kelas Lokal EkonomiDAOP IV Semarang)." Jurnal Gaussian 7, no. 1 (February 28, 2018): 96–109. http://dx.doi.org/10.14710/j.gauss.v7i1.26639.

Full text
Abstract:
Autoregressive Integrated Moving Average (ARIMA) is stationary time series model after differentiation. Differentiation value of ARIMA method is an integer so it is only able to model in the short term. The best model using ARIMA method is ARIMA([13]; 1; 0) with an MSE value of 1,870844. The Intervention method is a model for time series data which in practice has extreme fluctuations both up and down. In the data plot the number of train passengers was found to be extreme fluctuation. The data used was from January 2009 to June 2017 where fluctuation up significantly in January 2016 (T=85 to T=102) so the intervention model that was suspected was a step function. The best model uses the Intervention step function is ARIMA ([13]; 1; 1) (b=0; s=18; r=0) with MSE of 1124. Autoregressive Fractionally Integrated Moving Average (ARFIMA) method is a development of the ARIMA method. The advantage of the ARFIMA method is the non-integer differentiation value so that it can overcome long memory effect that can not be solve with the ARIMA method. ARFIMA model is capable of modeling high changes in the long term (long term persistence) and explain long-term and short-term correlation structures at the same time. The number of local economy class train passengers in DAOP IV Semarang contains long memory effects, so the ARFIMA method is used to obtain the best model. The best model obtained is the ARMA(0; [1,13]) model with the differential value is 0,367546, then the model can be written into ARFIMA (0; d; [1,13]) with an MSE value of 0,00964. Based on the analysis of the three methods, the best method of analyzing the number of local economy class train passengers in DAOP IV Semarang is the ARFIMA method with the model is ARFIMA (0; 0,367546; [1,13]). Keywords: Train Passengers, ARIMA, Intervention, ARFIMA, Forecasting
APA, Harvard, Vancouver, ISO, and other styles
2

Pfeifer, Phillip E., and Stuart Jay Deutsch. "Seasonal Space-Time ARIMA Modeling." Geographical Analysis 13, no. 2 (September 3, 2010): 117–33. http://dx.doi.org/10.1111/j.1538-4632.1981.tb00720.x.

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

Pack, David J. "In defense of ARIMA modeling." International Journal of Forecasting 6, no. 2 (July 1990): 211–18. http://dx.doi.org/10.1016/0169-2070(90)90006-w.

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

Ahmar, Ansari Saleh, Suryo Guritno, Abdurakhman, Abdul Rahman, Awi, Alimuddin, Ilham Minggi, et al. "Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO)." Journal of Physics: Conference Series 954 (January 2018): 012010. http://dx.doi.org/10.1088/1742-6596/954/1/012010.

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

N. N. Jambhulkar, N. N. Jambhulkar. "Modeling of Rice Production in Punjab using ARIMA Model." International Journal of Scientific Research 2, no. 8 (June 1, 2012): 1–2. http://dx.doi.org/10.15373/22778179/aug2013/1.

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

Susanti, Riana, and Askardiya Radmoyo Adji. "ANALISIS PERAMALAN IHSG DENGAN TIME SERIES MODELING ARIMA." Jurnal Manajemen Kewirausahaan 17, no. 1 (June 30, 2020): 97. http://dx.doi.org/10.33370/jmk.v17i1.393.

Full text
Abstract:
ABSTRAK Prediksi harga saham merupakan hal yang selalu menarik minat investor dan pemangku kepentingan lain terhadap pasar saham. Dalam perdagangan saham, pergerakan IHSG yang akan datang dapat digunakan sebagai dasar untuk melakukan pengambilan keputusan pelaku investasi. Penelitian ini bertujuan menguji model time series Autoregressive Integrated Moving Average (ARIMA) untuk memprediksi IHSG di Bursa Efek Indonesia. ARIMA adalah model untuk menghasilkan perkiraan dari data historis. Data dalam penelitian ini dikumpulkan dari IHSG harian dari 2 Januari 2017 sampai 3 Januari 2018. Data diperoleh dari laporan bulanan yang dipublikasikan Bursa Efek Indonesia. Hasil prediksi menunjukkan bahwa model ini cukup akurat untuk peramalan. Hasil penelitian menunjukkan bahwa model ARIMA yang memiliki kinerja terbaik untuk memprediksi IHSG, yaitu model ARIMA (7,3,1) Kata Kunci: Prediksi harga saham, IHSG, Time Series, ARIMA
APA, Harvard, Vancouver, ISO, and other styles
7

Agrienvi. "Frits Fahridws Damanik." Agrienvi, Jurnal Ilmu Pertanian 13, no. 02 (February 3, 2020): 1–8. http://dx.doi.org/10.36873/aev.v13i02.657.

Full text
Abstract:
ABSTRACTChili is one of the leading commodities of vegetables which has strategic value at national and regional levels.An unexpected increase in chili prices often results a surge of inflation and economic turmoil. Study and modeling ofchili production are needed as a planning and evaluation material for policy makers. One of the most frequently usedmethods in modeling and forecasting time series data is Autoregressive Integrated Moving Avarage (ARIMA). Theresults of ARIMA modeling on chili production data found that the data were unstationer conditions of the mean sothat must differenced while the data on the production of small chilli carried out the stages of data transformation anddifferencing due to the unstationer of data on variants and the mean. The best ARIMA model that can be appliedbased on the smallest AIC and MSE criteria for data on the amount of chili and small chilli production in CentralKalimantan Province is ARIMA (3,1,0).Keywords: modeling of chilli, forecasting of chilli, Autoregresive Integrated Moving Avarage, ARIMA, Box-Jenkins.
APA, Harvard, Vancouver, ISO, and other styles
8

Agrienvi. "DOI: https://doi.org/10.36873/ae , Frits Fahridws Damanik." Agrienvi: Jurnal Ilmu Pertanian 13, no. 02 (February 12, 2020): 1–8. http://dx.doi.org/10.36873/aev.v13i02.723.

Full text
Abstract:
ABSTRACTChili is one of the leading commodities of vegetables which has strategic value at national and regional levels.An unexpected increase in chili prices often results a surge of inflation and economic turmoil. Study and modeling ofchili production are needed as a planning and evaluation material for policy makers. One of the most frequently usedmethods in modeling and forecasting time series data is Autoregressive Integrated Moving Avarage (ARIMA). Theresults of ARIMA modeling on chili production data found that the data were unstationer conditions of the mean so thatmust differenced while the data on the production of small chilli carried out the stages of data transformation anddifferencing due to the unstationer of data on variants and the mean. The best ARIMA model that can be applied basedon the smallest AIC and MSE criteria for data on the amount of chili and small chilli production in Central KalimantanProvince is ARIMA (3,1,0).Keywords: modeling of chilli, forecasting of chilli, Autoregresive Integrated Moving Avarage, ARIMA, Box-Jenkins.
APA, Harvard, Vancouver, ISO, and other styles
9

SAADAT, SH, M. SALEM, M. GHORANNEVISS, and P. KHORSHID. "Stochastic modeling of plasma mode forecasting in tokamak." Journal of Plasma Physics 78, no. 2 (November 11, 2011): 99–104. http://dx.doi.org/10.1017/s0022377811000456.

Full text
Abstract:
AbstractThe structure of magnetohydrodynamic (MHD) modes has always been an interesting study in tokamaks. The mode number of tokamak plasma is the most important parameter, which plays a vital role in MHD instabilities. If it could be predicted, then the time of exerting external fields, such as feedback fields and Resonance Helical Field, could be obtained. Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average are useful models to predict stochastic processes. In this paper, we suggest using ARIMA model to forecast mode number. The ARIMA model shows correct mode number (m = 4) about 0.5 ms in IR-T1 tokamak and equations of Mirnov coil fluctuations are obtained. It is found that the recursive estimates of the ARIMA model parameters change as the plasma mode changes. A discriminator function has been proposed to determine plasma mode based on the recursive estimates of model parameters.
APA, Harvard, Vancouver, ISO, and other styles
10

Maxwell, Obubu, Ikediuwa Udoka Chinedu, Anabike Charles Ifeanyi, and Nwokike Chukwudike C. "On Modeling Murder Crimes in Nigeria." Scientific Review, no. 58 (August 1, 2018): 157–62. http://dx.doi.org/10.32861/sr.58.157.162.

Full text
Abstract:
This paper examines the modelling and forecasting Murder crimes using Auto-Regressive Integrated Moving Average models (ARIMA). Twenty-nine years data obtained from Nigeria Information Resource Center were used to make predictions. Among the most effective approaches for analyzing time series data is the method propounded by Box and Jenkins, the Autoregressive Integrated Moving Average (ARIMA). The augmented Dickey-Fuller test for unit root was applied to the data set to investigate for Stationarity, the data set was found to be non-stationary hence transformed using first-order differencing to make them Stationary. The Stationarities were confirmed with time series plots. Statistical analysis was performed using GRETL software package from which, ARIMA (0, 1, 0) was found to be the best and adequate model for Murder crimes. Forecasted values suggest that Murder would slightly be on the increase.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "ARIMA Modeling"

1

Mohamed, Fadil B. "Space-time ARIMA and transfer function-noise modeling of rainfall-runoff process." Thesis, University of Ottawa (Canada), 1985. http://hdl.handle.net/10393/4723.

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

Fang, Yanhui. "Flood Forecasting via a Combination of Stochastic ARIMA Approach and Deterministic HEC-RAS Modeling." Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1449142353.

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

Landström, Johan, and Patric Linderoth. "Precisionsbaserad analys av trafikprediktion med säsongsbaserad ARIMA-modellering." Thesis, Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-14336.

Full text
Abstract:
Intelligenta Transportsystem (ITS) utgör idag en central del i arbetet att försöka höja kvaliteten i transportnätverken, genom att exempelvis ge stöd i arbetet att leda trafik i realtid och att ge trafikanter större möjlighet att ta informerade beslut gällandes sin körning. Kortsiktig prediktion av trafikdata, däribland trafikvolym, spelar en central roll för de tjänster ITS-systemen levererar. Den starka teknologiska utvecklingen de senaste decennierna har bidragit till en ökad möjlighet till att använda datadriven modellering för att utföra kortsiktiga prediktioner av trafikdata. Säsongsbaserad ARIMA (SARIMA) är en av de vanligaste datadrivna modellerna för modellering och predicering av trafikdata, vilken använder mönster i historisk data för att predicera framtida värden. Vid modellering med SARIMA behöver en mängd beslut tas gällandes de data som används till modelleringen. Exempel på sådana beslut är hur stor mängd träningsdata som ska användas, vilka dagar som ska ingå i träningsmängden och vilket aggregationsintervall som ska användas. Därtill utförs nästintill enbart enstegsprediktioner i tidigare studier av SARIMA-modellering av trafikdata, trots att modellen stödjer predicering av flera steg in i framtiden. Besluten gällandes de parametrar som nämnts saknar ofta teoretisk motivering i tidigare studier, samtidigt som det är högst troligt att dessa beslut påverkar träffsäkerheten i prediktionerna. Därför syftar den här studien till att utföra en känslighetsanalys av dessa parametrar, för att undersöka hur olika värden påverkar precisionen vid prediktion av trafikvolym. I studien utvecklades en modell, med vilken data kunde importeras, preprocesseras och sedan modelleras med hjälp av SARIMA. Studien använde trafikvolymdata som insamlats under januari och februari 2014, med hjälp av kameror placerade på riksväg 40 i utkanten av Göteborg. Efter differentiering av data används såväl autokorrelations- och partiell autokorrelationsgrafer som informationskriterier för att definiera lämpliga SARIMA-modeller, med vilka prediktioner kunde göras. Med definierade modeller genomfördes ett experiment, där åtta unika scenarion testades för att undersöka hur prediktionsprecisionen av trafikvolym påverkades av olika mängder träningsdata, vilka dagar som ingick i träningsdata, längden på aggregationsintervallen och hur många tidssteg in i framtiden som predicerades. För utvärdering av träffsäkerheten i prediktionerna användes MAPE, RMSE och MAE. Resultaten som experimentet visar är att definierade SARIMA-modeller klarar att predicera aktuell data med god precision oavsett vilka värden som sattes för de variabler som studerades. Resultaten visade dock indikationer på att en träningsvolym omfattande fem dagar kan generera en modell som ger mer träffsäkra prediktioner än när volymer om 15 eller 30 dagar används, något som kan ha stor praktisk betydelse vid realtidsanalys. Därtill indikerar resultaten att samtliga veckodagar bör ingå i träningsdatasetet när dygnsvis säsongslängd används, att SARIMA-modelleringen hanterar aggregationsintervall om 60 minuter bättre än 30 eller 15 minuter samt att enstegsprediktioner är mer träffsäkra än när horisonter om en eller två dagar används. Studien har enbart fokuserat på inverkan av de fyra parametrarna var för sig och inte om en kombinerad effekt finns att hitta. Det är något som föreslås för framtida studier, liksom att vidare utreda huruvida en mindre träningsvolym kan fortsätta att generera mer träffsäkra prediktioner även för andra perioder under året.
Intelligent Transport Systems (ITS) today are a key part of the effort to try to improve the quality of transport networks, for example by supporting the real-time traffic management and giving road users greater opportunity to take informed decisions regarding their driving. Short-term prediction of traffic data, including traffic volume, plays a central role in the services delivered by ITS systems. The strong technological development has contributed to an increased opportunity to use data-driven modeling to perform short-term predictions of traffic data. Seasonal ARIMA (SARIMA) is one of the most common models for modeling and predicting traffic data, which uses patterns in historical data to predict future values. When modeling with SARIMA, a variety of decisions are required regarding he data used. Examples of such decisions are the amount of training data to be used, the days to be included in training data and the aggregation interval to be used. In addition, one-step predictions are performed most often in previous studies of SARIMA modeling of traffic data, although the model supports multi-step prediction into the future. Often, in previous studies, decisions are made concerning mentioned variables without theoretical motivation, while it is highly probable that these decisions affect the accuracy of the predictions. Therefore, this study aims at performing a sensitivity analysis of these parameters to investigate how different values affect the accuracy of traffic volume prediction. The study developed a model with which data could be imported, preprocessed and then modeled using a SARIMA model. Traffic volume data was used, which was collected during January and February 2014, using cameras located on highway 40 on the outskirts of Gothenburg. After differentiation of data, autocorrelation and partial autocorrelation graphs as well as information criteria are used to define appropriate SARIMA models, with which predictions could be made. With defined models, an experiment was conducted in which eight unique scenarios were tested to investigate how the prediction accuracy of traffic volume was influenced by different amount of exercise data, what days was included in training data, length of aggregation intervals, and how many steps into the future were predicted. To evaluate the accuracy of the predictions, MAPE, RMSE and MAE were used. The results of the experiment show that developed SARIMA models are able to predict current data with good precision no matter what values were set for the variables studied. However, the results showed indications that a training volume of five days can generate a model that provides more accurate predictions than when using 15 or 30-day volumes, which can be of great practical importance in real-time analysis. In addition, the results indicate that all weekdays should be included in the training data set when daily seasonality is used, SARIMA modeling handles aggregation intervals of 60 minutes better than 30 or 15 minutes, and that one-step predictions are more accurate than when one or two days horizons are used. The study has focused only on the impact of the four parameters separately and not if a combined effect could be found. Further research is proposed for investigating if combined effects could be found, as well as further investigating whether a lesser training volume can continue to generate more accurate predictions even for other periods of the year.
APA, Harvard, Vancouver, ISO, and other styles
4

Wu, Ling. "Stochastic Modeling and Statistical Analysis." Scholar Commons, 2010. https://scholarcommons.usf.edu/etd/1813.

Full text
Abstract:
The objective of the present study is to investigate option pricing and forecasting problems in finance. This is achieved by developing stochastic models in the framework of classical modeling approach. In this study, by utilizing the stock price data, we examine the correctness of the existing Geometric Brownian Motion (GBM) model under standard statistical tests. By recognizing the problems, we attempted to demonstrate the development of modified linear models under different data partitioning processes with or without jumps. Empirical comparisons between the constructed and GBM models are outlined. By analyzing the residual errors, we observed the nonlinearity in the data set. In order to incorporate this nonlinearity, we further employed the classical model building approach to develop nonlinear stochastic models. Based on the nature of the problems and the knowledge of existing nonlinear models, three different nonlinear stochastic models are proposed. Furthermore, under different data partitioning processes with equal and unequal intervals, a few modified nonlinear models are developed. Again, empirical comparisons between the constructed nonlinear stochastic and GBM models in the context of three data sets are outlined. Stochastic dynamic models are also used to predict the future dynamic state of processes. This is achieved by modifying the nonlinear stochastic models from constant to time varying coefficients, and then time series models are constructed. Using these constructed time series models, the prediction and comparison problems with the existing time series models are analyzed in the context of three data sets. The study shows that the nonlinear stochastic model 2 with time varying coefficients is robust with respect different data sets. We derive the option pricing formula in the context of three nonlinear stochastic models with time varying coefficients. The option pricing formula in the frame work of hybrid systems, namely, Hybrid GBM (HGBM) and hybrid nonlinear stochastic models are also initiated. Finally, based on our initial investigation about the significance of presented nonlinear stochastic models in forecasting and option pricing problems, we propose to continue and further explore our study in the context of nonlinear stochastic hybrid modeling approach.
APA, Harvard, Vancouver, ISO, and other styles
5

Wenzel, Anne. "Komponentenzerlegung des Regelleistungsbedarfs mit Methoden der Zeitreihenanalyse." Master's thesis, Universitätsbibliothek Chemnitz, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-66420.

Full text
Abstract:
Im Rahmen der Arbeit wurden die minutengenauen Daten des Regelleistungsbedarfs (Summe aus Sekundärregelleistung und Minutenreserve) der Monate April bis Dezember des Jahres 2009 einer Regelzone einer Zeitreihenanalyse unterzogen und in Komponenten gemäß dem klassischen Komponentenmodell zerlegt. Diese sind die Trendkomponente, ermittelt durch einen gleitenden Durchschnitt mit der Länge einer Stunde, weiterhin zwei periodische Komponenten mit der Periodenlänge einer Stunde sowie der Periodenlänge eines Tages und die Restkomponente, welche mit einem ARIMA(2,1,5)-Prozess modelliert wurde. In der Zukunft sollte das erstellte Modell des Regelleistungsbedarfs durch Hinzunahme einer jahreszeitlichen Komponente noch verbessert werden. Dies war im Rahmen der Arbeit nicht möglich, da keine Daten über einen Zeitraum von mehreren Jahren vorhanden waren. Zusätzlich kann geprüft werden, inwiefern mit dem Komponentenmodell Prognosen durchführbar sind. Dafür sollte die Trendkomponente anders gewählt werden, da sich der hier gewählte Weg zu sehr an den Daten orientiert. Der zweite Teil der Aufgabenstellung dieser Arbeit bestand im Identifizieren inhaltlicher Komponenten, also möglicher Zusammenhänge zwischen dem Regelleistungsbedarf und verschiedenen denkbaren Ursachen. Als potentielle Ursachen wurden der Lastverlauf sowie die Windenergieeinspeisung untersucht. Zwischen der Zeitreihe des Lastverlaufs und der des Regelleistungsbedarfs bestand eine leichte positive Korrelation, zwischen der Zeitreihe der Windenergieeinspeisung und der des Regelleistungsbedarfs eine geringe negative Korrelation.
APA, Harvard, Vancouver, ISO, and other styles
6

Pokhrel, Nawa Raj. "Statistical Analysis and Modeling of Cyber Security and Health Sciences." Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7703.

Full text
Abstract:
Being in the era of information technology, importance and applicability of analytical statistical model an interdisciplinary setting in the modern statistics have increased significantly. Conceptually understanding the vulnerabilities in statistical perspective helps to develop the set of modern statistical models and bridges the gap between cybersecurity and abstract statistical /mathematical knowledge. In this dissertation, our primary goal is to develop series of the strong statistical model in software vulnerability in conjunction with Common Vulnerability Scoring System (CVSS) framework. In nutshell, the overall research lies at the intersection of statistical modeling, cybersecurity, and data mining. Furthermore, we generalize the model of software vulnerability to health science particularly in the stomach cancer data. In the context of cybersecurity, we have applied the well-known Markovian process in the combination of CVSS framework to determine the overall network security risk. The developed model can be used to identify critical nodes in the host access graph where attackers may be most likely to focus. Based on that information, a network administrator can make appropriate, prioritized decisions for system patching. Further, a flexible risk ranking technique is described, where the decisions made by an attacker can be adjusted using a bias factor. The model can be generalized for use with complicated network environments. We have further proposed a vulnerability analytic prediction model based on linear and non-linear approaches via time series analysis. Using currently available data from National Vulnerability Database (NVD) this study develops and present sets of predictive model by utilizing Auto Regressive Moving Average (ARIMA), Artificial Neural Network (ANN), and Support Vector Machine (SVM) settings. The best model which provides the minimum error rate is selected for prediction of future vulnerabilities. In addition, we purpose a new philosophy of software vulnerability life cycle. It says that vulnerability saturation is a local phenomenon, and it possesses an increasing cyclic behavior within the software vulnerability life cycle. Based on the new philosophy of software vulnerability life cycle, we purpose new effective differential equation model to predict future software vulnerabilities by utilizing the vulnerability dataset of three major OS: Windows 7, Linux Kernel, and Mac OS X. The proposed analytical model is compared with existing models in terms of fitting and prediction accuracy. Finally, the predictive model not only applicable to predict future vulnerability but it can be used in the various domain such as engineering, finance, business, health science, and among others. For instance, we extended the idea on health science; to predict the malignant tumor size of stomach cancer as a function of age based on the given historical data from Surveillance Epidemiology and End Results (SEER).
APA, Harvard, Vancouver, ISO, and other styles
7

Simmons, Laurette Poulos. "The Development and Evaluation of a Forecasting System that Incorporates ARIMA Modeling with Autoregression and Exponential Smoothing." Thesis, North Texas State University, 1985. https://digital.library.unt.edu/ark:/67531/metadc332047/.

Full text
Abstract:
This research was designed to develop and evaluate an automated alternative to the Box-Jenkins method of forecasting. The study involved two major phases. The first phase was the formulation of an automated ARIMA method; the second was the combination of forecasts from the automated ARIMA with forecasts from two other automated methods, the Holt-Winters method and the Stepwise Autoregressive method. The development of the automated ARIMA, based on a decision criterion suggested by Akaike, borrows heavily from the work of Ang, Chuaa and Fatema. Seasonality and small data set handling were some of the modifications made to the original method to make it suitable for use with a broad range of time series. Forecasts were combined by means of both the simple average and a weighted averaging scheme. Empirical and generated data were employed to perform the forecasting evaluation. The 111 sets of empirical data came from the M-Competition. The twenty-one sets of generated data arose from ARIMA models that Box, Taio and Pack analyzed using the Box-Jenkins method. To compare the forecasting abilities of the Box-Jenkins and the automated ARIMA alone and in combination with the other two methods, two accuracy measures were used. These measures, which are free of magnitude bias, are the mean absolute percentage error (MAPE) and the median absolute percentage error (Md APE).
APA, Harvard, Vancouver, ISO, and other styles
8

Sampaio, Júnior Roberto Antônio de Oliveira. "Modelagem matemática para consciência financeira e a bolsa de valores." reponame:Repositório Institucional da UFABC, 2018.

Find full text
Abstract:
Orientador: Prof. Dr. André Ricardo Oliveira da Fonseca
Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Matemática , Santo André, 2018.
O intuito desse trabalho é fomentar o estudo da matemática financeira com o objetivo de um impacto social, para que os alunos de baixa renda atinjam uma consciência financeira maior durante sua formação escolar e construção de sua família. Esse estudo tem motivação pessoal e também éj ustificado pela falta de interesse dos alunos em assuntos de Álgebra, Lógica e Abstração. Através de modelos financeiros da modelagem matemática e de ferramentas computacionais, apresentados na forma de atividades para o Ensino Médio, espera-se uma conscientização maior do aluno em relação à sua liberdade financeira.
The purpose of this work is to promote the study of financial mathematics with the objective of a social impact so that the students of low income achieve a greater financial consistency during their school formation and construction of their family. This study has personal motivation and is also justified by students¿ lack of interest in Algebra, Logic, and Abstraction. Through financial models, mathematical modeling and computational tools, presented in the form of activities for High School, it is expected that students will become more aware of their financial freedom.
APA, Harvard, Vancouver, ISO, and other styles
9

AJMAL, KHAN, and MAHMOOD HASHMI TAHIR. "Daily Calls Volume Forecasting." Thesis, Högskolan Dalarna, Statistik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:du-4852.

Full text
Abstract:
A massive amount has been written about forecasting but few articles are written about the development of time series models of call volumes for emergency services. In this study, we use different techniques for forecasting and make the comparison of the techniques for the call volume of the emergency service Rescue 1122 Lahore, Pakistan. For the purpose of this study data is taken from emergency calls of Rescue 1122 from 1st January 2008 to 31 December 2009 and 731 observations are used. Our goal is to develop a simple model that could be used for forecasting the daily call volume. Two different approaches are used for forecasting the daily call volume Box and Jenkins (ARIMA) methodology and Smoothing methodology. We generate the models for forecasting of call volume and present a comparison of the two different techniques.
APA, Harvard, Vancouver, ISO, and other styles
10

Santana, Delano Mendes de. "Abordagem MRL, Arima e Data Mining para otimização de custos no suprimento energético em plantas petroquímicas." reponame:Repositório Institucional da UFABC, 2018.

Find full text
Abstract:
Orientador: Prof. Dr. Douglas Alves Cassiano
Coorientador: Prof. Dr. Sérgio Ricardo Lourenço
Tese (doutorado) - Universidade Federal do ABC. Programa de Pós-Graduação em Energia, Santo André, 2018.
Uma forma de otimização dos recursos energéticos de uma planta petroquímica é a utilização de Mix Integer Linear Programing (MILP) para decisão da configuração ótima do acionamento dos equipamentos da unidade. Entretanto uma questão ainda em aberto é qual a correlação existente entre a série temporal destes ganhos energéticos com o preço da energia no mercado livre, a temperatura ambiente, a carga da planta e a demanda elétrica desta planta petroquímica. Dessa forma, o objetivo deste trabalho foi obter a correlação entre estas variáveis. A metodologia utilizada contou com três abordagens de exploração de correlações, a primeira foi a Modelagem de Regressão Linear (MRL), a segunda a Autoregressive Integrated Moving Average (ARIMA) e, a terceira, a Data Mining. Como principais resultados foram obtidas as correlações entre estas variáveis pelas três abordagens, além da comparação das regressões em termos de: qualidade de ajuste do modelo; visualização dos dados e aplicação em aplicativos comuns como o Excel®. Adicionalmente foram descobertos padrões escondidos nos dados e gerou-se conhecimento acadêmico capaz de suportar decisões industriais que conduzam a melhorias de eficiência energética.
Is possible to optimize the energy resources of a petrochemical plant using Mix Integer Linear Programing (MILP) to decide the optimal configuration of the equipment. However, a still open question is what correlation exists between the time series of these energy savings with the price of energy in the free market, the ambient temperature, the plant load and the electric demand of this petrochemical plant. The objective of this study is to obtain the correlation between these variables. Three approaches was used, Linear Regression Modeling (LRM), Autoregressive Integrated Moving Average (ARIMA) and Data Mining. Were obtained the correlations between these variables by the three approaches, besides the comparison of the regressions in terms of: adherence to the real values; data visualization and application in common applications like Excel®. In addition, hidden patterns were discovered in the data and academic knowledge was generated, supporting industrial decisions that lead to improvements in energy efficiency.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "ARIMA Modeling"

1

Santo, Paul S. Dal. System identification by ARMA modeling. Monterey, California: Naval Postgraduate School, 1988.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Solano, Carlos Hernando Velasco. ARMA modeling of signals in the time domain. Monterey, Calif: Naval Postgraduate School, 1992.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Fargues, Monique P. TLS-based prefiltering technique for time-domain ARMA modeling. Monterey, Calif: Naval Postgraduate School, 1994.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Therrien, Charles W. An iterative extension of Prony's method for ARMA signal modeling. Monterey, Calif: Naval Postgraduate School, 1993.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

McCleary, Richard, David McDowall, and Bradley J. Bartos. Noise Modeling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0003.

Full text
Abstract:
Chapter 3 introduces the Box-Jenkins AutoRegressive Integrated Moving Average (ARIMA) noise modeling strategy. The strategy begins with a test of the Normality assumption using a Kolomogov-Smirnov (KS) statistic. Non-Normal time series are transformed with a Box-Cox procedure is applied. A tentative ARIMA noise model is then identified from a sample AutoCorrelation function (ACF). If the sample ACF identifies a nonstationary model, the time series is differenced. Integer orders p and q of the underlying autoregressive and moving average structures are then identified from the ACF and partial autocorrelation function (PACF). Parameters of the tentative ARIMA noise model are estimated with maximum likelihood methods. If the estimates lie within the stationary-invertible bounds and are statistically significant, the residuals of the tentative model are diagnosed to determine whether the model’s residuals are not different than white noise. If the tentative model’s residuals satisfy this assumption, the statistically adequate model is accepted. Otherwise, the identification-estimation-diagnosis ARIMA noise model-building strategy continues iteratively until it yields a statistically adequate model. The Box-Jenkins ARIMA noise modeling strategy is illustrated with detailed analyses of twelve time series. The example analyses include non-Normal time series, stationary white noise, autoregressive and moving average time series, nonstationary time series, and seasonal time series. The time series models built in Chapter 3 are re-introduced in later chapters. Chapter 3 concludes with a discussion and demonstration of auxiliary modeling procedures that are not part of the Box-Jenkins strategy. These auxiliary procedures include the use of information criteria to compare models, unit root tests of stationarity, and co-integration.
APA, Harvard, Vancouver, ISO, and other styles
6

McCleary, Richard, David McDowall, and Bradley J. Bartos. Intervention Modeling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0005.

Full text
Abstract:
The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.
APA, Harvard, Vancouver, ISO, and other styles
7

McCleary, Richard, David McDowall, and Bradley J. Bartos. Forecasting. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0004.

Full text
Abstract:
Chapter 4 downplays forecasting’s role in the design and analysis of time series experiments and emphasizes its potential abuses. While the “best” ARIMA model will outperform other forecasting models in the short and medium-run, long-horizon ARIMA forecasts grow increasingly inaccurate with diminished utility to the forecaster. Although the principles of forecasting help provide deeper insight into the nature of ARIMA models and modeling, the forecasts themselves are ordinarily of limited practical value. Forecasting can provide useful guidance to analysts choosing between two competing univariate models. While forecasting accuracy is only one of many criteria that might be considered, other things being equal, it is fair to say that a statistically adequate model of a process should provide reasonable forecasts of the future. Forecast accuracy depends on a host of factors, many of which lie outside the grasp of model adequacy. More important, forecast accuracy has no universally accepted metric.
APA, Harvard, Vancouver, ISO, and other styles
8

Kayahan, Gurhan. ARMA modeling. 1988.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

Luo, Xiaoguang. GPS Stochastic Modelling: Signal Quality Measures and ARMA Processes. Springer, 2016.

Find full text
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "ARIMA Modeling"

1

Wibowo, Wahyu, Sarirazty Dwijantari, and Alia Hartati. "Time Series Machine Learning: Implementing ARIMA and Hybrid ARIMA-ANN for Electricity Forecasting Modeling." In Communications in Computer and Information Science, 126–39. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7242-0_11.

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

Ismail, Mohd Tahir, Nur Zulaika Abu Shah, and Samsul Ariffin Abdul Karim. "Modeling Solar Radiation in Peninsular Malaysia Using ARIMA Model." In Clean Energy Opportunities in Tropical Countries, 53–71. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9140-2_3.

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

Klazoglou, Paraskevi, and Nikolaos Dritsakis. "Modeling and Forecasting of US Health Expenditures Using ARIMA Models." In Advances in Panel Data Analysis in Applied Economic Research, 457–72. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-70055-7_36.

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

Sousa-Vieira, Maria-Estrella, Andrés Suárez-González, José-Carlos López-Ardao, and Cándido López-García. "Efficient On-Line Generation of the Correlation Structure of F-ARIMA Processes." In Analytical and Stochastic Modeling Techniques and Applications, 131–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02205-0_10.

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

Pannakkong, Warut, and Van-Nam Huynh. "A Hybrid Model of ARIMA and ANN with Discrete Wavelet Transform for Time Series Forecasting." In Modeling Decisions for Artificial Intelligence, 159–69. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67422-3_14.

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

Badrinath Krishna, Varun, Ravishankar K. Iyer, and William H. Sanders. "ARIMA-Based Modeling and Validation of Consumption Readings in Power Grids." In Critical Information Infrastructures Security, 199–210. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33331-1_16.

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

Dadhich, Manish, Manvinder Singh Pahwa, Vipin Jain, and Ruchi Doshi. "Predictive Models for Stock Market Index Using Stochastic Time Series ARIMA Modeling in Emerging Economy." In Advances in Mechanical Engineering, 281–90. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0942-8_26.

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

Supatmi, Sri, Rongtao Huo, and Irfan Dwiguna Sumitra. "Implementation of Multiplicative Seasonal ARIMA Modeling and Flood Prediction Based on Long-Term Time Series Data in Indonesia." In Lecture Notes in Computer Science, 38–50. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24265-7_4.

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

Liu, Timina, Shuangzhe Liu, and Lei Shi. "ARIMA Modelling and Forecasting." In Time Series Analysis Using SAS Enterprise Guide, 61–85. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0321-4_4.

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

Aljandali, Abdulkader, and Motasam Tatahi. "Economic Forecasting using ARIMA Modelling." In Economic and Financial Modelling with EViews, 111–42. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92985-9_7.

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

Conference papers on the topic "ARIMA Modeling"

1

Alghamdi, Taghreed, Khalid Elgazzar, Magdi Bayoumi, Taysseer Sharaf, and Sumit Shah. "Forecasting Traffic Congestion Using ARIMA Modeling." In 2019 15th International Wireless Communications and Mobile Computing Conference (IWCMC). IEEE, 2019. http://dx.doi.org/10.1109/iwcmc.2019.8766698.

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

Mohamadi, Salman, Hamidreza Amindavar, and S. M. Ali Tayaranian Hosseini. "ARIMA-GARCH modeling for epileptic seizure prediction." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952305.

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

Kern, Joshua V., John B. Ferris, David Gorsich, and Alexander A. Reid. "Characterizing 2D road profiles using ARIMA modeling techniques." In Defense and Security Symposium, edited by Kevin Schum and Dawn A. Trevisani. SPIE, 2007. http://dx.doi.org/10.1117/12.720088.

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

Stoimenova-Minova, M. "Hybrid CART-ARIMA approach for PM10 pollutant modeling." In APPLICATION OF MATHEMATICS IN TECHNICAL AND NATURAL SCIENCES: 12th International On-line Conference for Promoting the Application of Mathematics in Technical and Natural Sciences - AMiTaNS’20. AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0033736.

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

Ma, Jungmok, and Harrison M. Kim. "Predictive Modeling of Product Returns for Remanufacturing." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46875.

Full text
Abstract:
As awareness of environmental issues increases, the pressures from the public and policy makers have forced OEMs to consider remanufacturing as the key product design option. In order to make the remanufacturing operations more profitable, forecasting product returns is critical with regards to the uncertainty in quantity and timing. This paper proposes a predictive model selection algorithm to deal with the uncertainty by identifying better predictive models. Unlike other major approaches in literature (distributed lag model or DLM), the predictive model selection algorithm focuses on the predictive power over new or future returns. The proposed algorithm extends the set of candidate models that should be considered: autoregressive integrated moving average or ARIMA (previous returns for future returns), DLM (previous sales for future returns), and mixed model (both previous sales and returns for future returns). The prediction performance measure from holdout samples is used to find a better model among them. The case study of reusable bottles shows that one of the candidate models, ARIMA, can predict better than the DLM depending on the relationships between returns and sales. The univariate model is widely unexplored due to the criticism that the model cannot utilize the previous sales. Another candidate model, mixed model, can provide a chance to find a better predictive model by combining the ARIMA and DLM. The case study also shows that the DLM in the predictive model selection algorithm can provide a good predictive performance when there are relatively strong and static relationships between returns and sales.
APA, Harvard, Vancouver, ISO, and other styles
6

Phinikarides, Alexander, George Makrides, Nitsa Kindyni, Andreas Kyprianou, and George E. Georghiou. "ARIMA modeling of the performance of different photovoltaic technologies." In 2013 IEEE 39th Photovoltaic Specialists Conference (PVSC). IEEE, 2013. http://dx.doi.org/10.1109/pvsc.2013.6744268.

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

Cao, Yingyu, Ting Cao, Huang Ye, Yang Yan, and Jiafu Chu. "ARIMA Prediction Model-based Cluster Algorithm in Ad Hoc Networks." In 2nd International Conference on Computer Application and System Modeling. Paris, France: Atlantis Press, 2012. http://dx.doi.org/10.2991/iccasm.2012.34.

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

Zhu Haoyun and Chen Xu. "Sichuan Province service industry development forecast - based on ARIMA model." In 2010 International Conference on Computer Application and System Modeling (ICCASM 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccasm.2010.5620594.

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

Banaezadeh, Fatemeh. "ARIMA-modeling based prediction mechanism in object tracking sensor networks." In 2015 7th Conference on Information and Knowledge Technology (IKT). IEEE, 2015. http://dx.doi.org/10.1109/ikt.2015.7288737.

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

Wagner, Shannon M., and John B. Ferris. "A polynomial chaos approach to ARIMA modeling and terrain characterization." In Defense and Security Symposium, edited by Kevin Schum and Dawn A. Trevisani. SPIE, 2007. http://dx.doi.org/10.1117/12.720081.

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

Reports on the topic "ARIMA Modeling"

1

Therrien, Charles W., and Carlos H. Velasco. An Iterative Extension of Prony's Method for ARMA Signal Modeling. Fort Belvoir, VA: Defense Technical Information Center, September 1993. http://dx.doi.org/10.21236/ada278841.

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

Carriere, R., and R. L. Moses. High Resolution Radar Target Modeling Using ARMA (Autoregressive Moving Average)Models. Fort Belvoir, VA: Defense Technical Information Center, April 1989. http://dx.doi.org/10.21236/ada218212.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'ARIMA Modeling' for particular source types:
Journal articles Dissertations / Theses
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