Готові списки джерел за темами / Multivariate time series forecasting

Добірка наукової літератури з теми "Multivariate time series forecasting"

Автор: Grafiati
Опубліковано: 10 травня 2022
Оновлено: 24 січня 2023

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  1. Статті в журналах
  2. Дисертації
  3. Книги
  4. Частини книг
  5. Тези доповідей конференцій
  6. Звіти організацій

Статті в журналах з теми "Multivariate time series forecasting":

1

Athanasopoulos, George, and Farshid Vahid. "Forecasting multivariate time series." International Journal of Forecasting 31, no. 3 (July 2015): 680–81. http://dx.doi.org/10.1016/j.ijforecast.2015.03.003.

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2

Wang, Miss Lei. "Advanced Multivariate Time Series Forecasting Models." Journal of Mathematics and Statistics 14, no. 1 (January 1, 2018): 253–60. http://dx.doi.org/10.3844/jmssp.2018.253.260.

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3

Şişman-Yılmaz, N. Arzu, Ferda N. Alpaslan, and Lakhmi Jain. "ANFISunfoldedintime for multivariate time series forecasting." Neurocomputing 61 (October 2004): 139–68. http://dx.doi.org/10.1016/j.neucom.2004.03.009.

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4

Duan, Ziheng, Haoyan Xu, Yida Huang, Jie Feng, and Yueyang Wang. "Multivariate Time Series Forecasting with Transfer Entropy Graph." Tsinghua Science and Technology 28, no. 1 (February 2023): 141–49. http://dx.doi.org/10.26599/tst.2021.9010081.

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5

Shapovalova, Yuliya, Nalan Baştürk, and Michael Eichler. "Multivariate Count Data Models for Time Series Forecasting." Entropy 23, no. 6 (June 5, 2021): 718. http://dx.doi.org/10.3390/e23060718.

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Count data appears in many research fields and exhibits certain features that make modeling difficult. Most popular approaches to modeling count data can be classified into observation and parameter-driven models. In this paper, we review two models from these classes: the log-linear multivariate conditional intensity model (also referred to as an integer-valued generalized autoregressive conditional heteroskedastic model) and the non-linear state-space model for count data. We compare these models in terms of forecasting performance on simulated data and two real datasets. In simulations, we consider the case of model misspecification. We find that both models have advantages in different situations, and we discuss the pros and cons of inference for both models in detail.
6

Yin, Yi, and Pengjian Shang. "Forecasting traffic time series with multivariate predicting method." Applied Mathematics and Computation 291 (December 2016): 266–78. http://dx.doi.org/10.1016/j.amc.2016.07.017.

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7

Thomakos, Dimitrios D., and Konstantinos Nikolopoulos. "Forecasting Multivariate Time Series with the Theta Method." Journal of Forecasting 34, no. 3 (February 26, 2015): 220–29. http://dx.doi.org/10.1002/for.2334.

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8

Shih, Shun-Yao, Fan-Keng Sun, and Hung-yi Lee. "Temporal pattern attention for multivariate time series forecasting." Machine Learning 108, no. 8-9 (June 11, 2019): 1421–41. http://dx.doi.org/10.1007/s10994-019-05815-0.

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9

Van Der Knoop, H. S. "Conditional forecasting with a multivariate time series model." Economics Letters 22, no. 2-3 (January 1986): 233–36. http://dx.doi.org/10.1016/0165-1765(86)90238-7.

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10

Ji, Xin, Haifeng Zhang, Jianfang Li, Xiaolong Zhao, Shouchao Li, and Rundong Chen. "Multivariate time series prediction of high dimensional data based on deep reinforcement learning." E3S Web of Conferences 256 (2021): 02038. http://dx.doi.org/10.1051/e3sconf/202125602038.

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In order to improve the prediction accuracy of high-dimensional data time series, a high-dimensional data multivariate time series prediction method based on deep reinforcement learning is proposed. The deep reinforcement learning method is used to solve the time delay of each variable and mine the data characteristics. According to the principle of maximum conditional entropy, the embedding dimension of the phase space is expanded, and a multivariate time series model of high-dimensional data is constructed. Thus, the conversion of reconstructed coordinates from low-dimensional to high-dimensional can be kept relatively stable. The strong independence and low redundancy of the final reconstructed phase space construct an effective model input vector for multivariate time series forecasting. Numerical experiments of classical multivariable chaotic time series show that the method proposed in this paper has better forecasting effect, which shows the forecasting effectiveness of this method.
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Статті в журналах Дисертації Книги

Дисертації з теми "Multivariate time series forecasting":

1

Qiang, Fu. "Bayesian multivariate time series models for forecasting European macroeconomic series." Thesis, University of Hull, 2000. http://hydra.hull.ac.uk/resources/hull:8068.

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Research on and debate about 'wise use' of explicitly Bayesian forecasting procedures has been widespread and often heated. This situation has come about partly in response to the dissatisfaction with the poor forecasting performance of conventional methods and partly in view of the development of computational capacity and macro-data availability. Experience with Bayesian econometric forecasting schemes is still rather limited, but it seems to be an attractive alternative to subjectively adjusted statistical models [see, for example, Phillips (1995a), Todd (1984) and West & Harrison (1989)]. It provides effective standards of forecasting performance and has demonstrated success in forecasting macroeconomic variables. Therefore, there would seem a case for seeking some additional insights into the important role of such methods in achieving objectives within the macroeconomics profession. The primary concerns of this study, motivated by the apparent deterioration of mainstream macroeconometric forecasts of the world economy in recent years [Wallis (1989), pp.34-43], are threefold. The first is to formalize a thorough, yet simple, methodological framework for empirical macroeconometric modelling in a Bayesian spirit. The second is to investigate whether improved forecasting accuracy is feasible within a European-based multicountry context. This is conducted with particular emphasis on the construction and implementation of Bayesian vector autoregressive (BVAR) models that incorporate both a priori and cointegration restrictions. The third is to extend the approach and apply it to the joint-modelling of system-wide interactions amongst national economies. The intention is to attempt to generate more accurate answers to a variety of practical questions about the future path towards a united Europe. The use of BVARs has advanced considerably. In particular, the value of joint-modelling with time-varying parameters and much more sophisticated prior distributions has been stressed in the econometric methodology literature. See e.g. Doan et al. (1984). Kadiyala and Karlsson (1993, 1997), Litterman (1986a), and Phillips (1995a, 1995b). Although trade-linked multicountry macroeconomic models may not be able to clarify all the structural and finer economic characteristics of each economy, they do provide a flexible and adaptable framework for analysis of global economic issues. In this thesis, the forecasting record for the main European countries is examined using the 'post mortem' of IMF, DECO and EEC sources. The formulation, estimation and selection of BVAR forecasting models, carried out using Microfit, MicroTSP, PcGive and RATS packages, are reported. Practical applications of BVAR models especially address the issues as to whether combinations of forecasts explicitly outperform the forecasts of a single model, and whether the recent failures of multicountry forecasts can be attributed to an increase in the 'internal volatility' of the world economic environment. See Artis and Holly (1992), and Barrell and Pain (1992, p.3). The research undertaken consolidates existing empirical and theoretical knowledge of BVAR modelling. It provides a unified coverage of economic forecasting applications and develops a common, effective and progressive methodology for the European economies. The empirical results reflect that in simulated 'out-of-sample' forecasting performances, the gains in forecast accuracy from imposing prior and long-run constraints are statistically significant, especially for small estimation sample sizes and long forecast horizons.
2

Katardjiev, Nikola. "High-variance multivariate time series forecasting using machine learning." Thesis, Uppsala universitet, Institutionen för informatik och media, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-353827.

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There are several tools and models found in machine learning that can be used to forecast a certain time series; however, it is not always clear which model is appropriate for selection, as different models are suited for different types of data, and domain-specific transformations and considerations are usually required. This research aims to examine the issue by modeling four types of machine- and deep learning algorithms - support vector machine, random forest, feed-forward neural network, and a LSTM neural network - on a high-variance, multivariate time series to forecast trend changes one time step in the future, accounting for lag.The models were trained on clinical trial data of patients in an alcohol addiction treatment plan provided by a Uppsala-based company. The results showed moderate performance differences, with a concern that the models were performing a random walk or naive forecast. Further analysis was able to prove that at least one model, the feed-forward neural network, was not undergoing this and was able to make meaningful forecasts one time step into the future. In addition, the research also examined the effec tof optimization processes by comparing a grid search, a random search, and a Bayesian optimization process. In all cases, the grid search found the lowest minima, though its slow runtimes were consistently beaten by Bayesian optimization, which contained only slightly lower performances than the grid search.
Det finns flera verktyg och modeller inom maskininlärning som kan användas för att utföra tidsserieprognoser, men det är sällan tydligt vilken modell som är lämplig vid val, då olika modeller är anpassade för olika sorts data. Denna forskning har som mål att undersöka problemet genom att träna fyra modeller - support vector machine, random forest, ett neuralt nätverk, och ett LSTM-nätverk - på en flervariabelstidserie med hög varians för att förutse trendskillnader ett tidssteg framåt i tiden, kontrollerat för tidsfördröjning. Modellerna var tränade på klinisk prövningsdata från patienter som deltog i en alkoholberoendesbehandlingsplan av ett Uppsalabaserat företag. Resultatet visade vissa moderata prestandaskillnader, och en oro fanns att modellerna utförde en random walk-prognos. I analysen upptäcktes det dock att den ena neurala nätverksmodellen inte gjorde en sådan prognos, utan utförde istället meningsfulla prediktioner. Forskningen undersökte även effekten av optimiseringsprocesser genomatt jämföra en grid search, random search, och Bayesisk optimisering. I alla fall hittade grid search lägsta minimumpunkten, men dess långsamma körtider blev konsistent slagna av Bayesisk optimisering, som även presterade på nivå med grid search.
3

Lima, Diego Duarte. "A study of demand forecasting cashew trade in Cearà through multivariate time series." Universidade Federal do CearÃ, 2013. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=12185.

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nÃo hÃ
The application of time series in varius areas such as engineering, logistics, operations research and economics, aims to provide the knowledge of the dependency between observations, trends, seasonality and forecasts. Considering the lack of effective supporting methods od logistics planning in the area of foreign trade, the multivariate models habe been presented and used in this work, in the area of time series: vector autoregression (VAR), vector autoregression moving-average (VARMA) and state-space integral equation (SS). These models were used for the analysis of demand forecast, the the bivariate series of value and volume of cashew nut exports from Cearà from 1996 to 2012. The results showed that the model state space was more successful in predicting the variables value and volume over the period that goes from january to march 2013, when compared to other models by the method of root mean squared error, getting the lowest values for those criteria.
A aplicaÃÃo de sÃries temporais em diversas Ãreas como engenharia, logÃstica, pesquisa operacional e economia, tem como objetivo o conhecimento da dependÃncia entre dados, suas possÃveis tendÃncias, sazonalidades e a previsÃo de dados futuros. Considerando a carÃncia de mÃtodos eficazes de suporte ao planejamento logÃstico na Ãrea de comÃrcio exterior, neste trabalho foram apresentados e utilizados os modelos multivariados, na Ãrea de sÃries temporais: auto-regressivo vetorial (VAR), auto-regressivomÃdias mÃveis vetorial (ARMAV) e espaÃo de estados (EES). Estes modelos foram empregados para a anÃlise de previsÃo de demanda, da sÃrie bivaria de valor e volume das exportaÃÃes cearenses de castanha de caju no perÃodo de 1996 à 2012. Os resultados mostraram que o modelo espaÃo de estados foi mais eficiente na previsÃo das variÃveis valor e volume ao longo do perÃodo janeiro à marÃo de 2013, quando comparado aos demais modelos pelo mÃtodo da raiz quadrada do erro mÃdio quadrÃtico, obtendo os menores valores para o referido critÃrio.
4

Larsson, Klara, and Freja Ling. "Time Series forecasting of the SP Global Clean Energy Index using a Multivariate LSTM." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301904.

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Clean energy and machine learning are subjects that play significant roles in shaping our future. The current climate crisis has forced the world to take action towards more sustainable solutions. Arrangements such as the UN’s Sustainable Development Goals and the Paris Agreement are causing an increased interest in renewable energy solutions. Further, the EU Taxonomy Regulation, applied in 2020, aims to scale up sustainable investments and to direct cash flows toward sustainable projects and activities. These measures create interest in investing in renewable energy alternatives and predicting future movements of stocks related to these businesses. Machine learning models have previously been used to predict time series with promising results. However, predicting time series in the form of stock price indices has, throughout previous attempts, proved to be a difficult task due to the complexity of the variables that play a role in the indices’ movements. This paper uses the machine learning algorithm long short-term memory (LSTM) to predict the S&P Global Clean Energy Index. The research question revolves around how well the LSTM model performs on this specific index and how the result is affected when past returns from correlating variables are added to the model. The researched variables are crude oil price, gold price, and interest. A model for each correlating variable was created, as well as one with all three, and one standard model which used only historical data from the index. The study found that while the model with the variable which had the strongest correlation performed best among the multivariate models, the standard model using only the target variable gave the most accurate result of any of the LSTM models.
Den pågående klimatkrisen har tvingat allt fler länder till att vidta åtgärder, och FN:s globala hållbarhetsmål och Parisavtalet ökar intresset för förnyelsebar energi. Vidare lanserade EU-kommissionen den 21 april 2021 ett omfattande åtgärdspaket, med syftet att öka investeringar i hållbara verksamheter. Detta skapar i sin tur ett ökat intresse för investeringar i förnyelsebar energi och metoder för att förutspå aktiepriser för dessa bolag. Maskininlärningsmodeller har tidigare använts för tidsserieanalyser med goda resultat, men att förutspå aktieindex har visat sig svårt till stor del på grund av uppgiftens komplexitet och antalet variabler som påverkar börsen. Den här uppsatsen använder sig av maskininlärningsmodellen long short-term memory (LSTM) för att förutspå S&P:s Global Clean Energy Index. Syftet är att ta reda på hur träffsäkert en LSTM-modell kan förutspå detta index, och hur resultatet påverkas då modellen används med ytterligare variabler som korrelerar med indexet. De variabler som undersöks är priset på råolja, priset på guld, och ränta. Modeller för var variabel skapades, samt en modell med samtliga variabler och en med endast historisk data från indexet. Resultatet visar att den modell med den variabel som korrelerar starkast med indexet presterade bäst bland flervariabelmodellerna, men den modell som endast användes med historisk data från indexet gav det mest träffsäkra resultatet.
5

Saluja, Rohit. "Interpreting Multivariate Time Series for an Organization Health Platform." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289465.

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Machine learning-based systems are rapidly becoming popular because it has been realized that machines are more efficient and effective than humans at performing certain tasks. Although machine learning algorithms are extremely popular, they are also very literal and undeviating. This has led to a huge research surge in the field of interpretability in machine learning to ensure that machine learning models are reliable, fair, and can be held liable for their decision-making process. Moreover, in most real-world problems just making predictions using machine learning algorithms only solves the problem partially. Time series is one of the most popular and important data types because of its dominant presence in the fields of business, economics, and engineering. Despite this, interpretability in time series is still relatively unexplored as compared to tabular, text, and image data. With the growing research in the field of interpretability in machine learning, there is also a pressing need to be able to quantify the quality of explanations produced after interpreting machine learning models. Due to this reason, evaluation of interpretability is extremely important. The evaluation of interpretability for models built on time series seems completely unexplored in research circles. This thesis work focused on achieving and evaluating model agnostic interpretability in a time series forecasting problem.  The use case discussed in this thesis work focused on finding a solution to a problem faced by a digital consultancy company. The digital consultancy wants to take a data-driven approach to understand the effect of various sales related activities in the company on the sales deals closed by the company. The solution involved framing the problem as a time series forecasting problem to predict the sales deals and interpreting the underlying forecasting model. The interpretability was achieved using two novel model agnostic interpretability techniques, Local interpretable model- agnostic explanations (LIME) and Shapley additive explanations (SHAP). The explanations produced after achieving interpretability were evaluated using human evaluation of interpretability. The results of the human evaluation studies clearly indicate that the explanations produced by LIME and SHAP greatly helped lay humans in understanding the predictions made by the machine learning model. The human evaluation study results also indicated that LIME and SHAP explanations were almost equally understandable with LIME performing better but with a very small margin. The work done during this project can easily be extended to any time series forecasting or classification scenario for achieving and evaluating interpretability. Furthermore, this work can offer a very good framework for achieving and evaluating interpretability in any machine learning-based regression or classification problem.
Maskininlärningsbaserade system blir snabbt populära eftersom man har insett att maskiner är effektivare än människor när det gäller att utföra vissa uppgifter. Även om maskininlärningsalgoritmer är extremt populära, är de också mycket bokstavliga. Detta har lett till en enorm forskningsökning inom området tolkbarhet i maskininlärning för att säkerställa att maskininlärningsmodeller är tillförlitliga, rättvisa och kan hållas ansvariga för deras beslutsprocess. Dessutom löser problemet i de flesta verkliga problem bara att göra förutsägelser med maskininlärningsalgoritmer bara delvis. Tidsserier är en av de mest populära och viktiga datatyperna på grund av dess dominerande närvaro inom affärsverksamhet, ekonomi och teknik. Trots detta är tolkningsförmågan i tidsserier fortfarande relativt outforskad jämfört med tabell-, text- och bilddata. Med den växande forskningen inom området tolkbarhet inom maskininlärning finns det också ett stort behov av att kunna kvantifiera kvaliteten på förklaringar som produceras efter tolkning av maskininlärningsmodeller. Av denna anledning är utvärdering av tolkbarhet extremt viktig. Utvärderingen av tolkbarhet för modeller som bygger på tidsserier verkar helt outforskad i forskarkretsar. Detta uppsatsarbete fokuserar på att uppnå och utvärdera agnostisk modelltolkbarhet i ett tidsserieprognosproblem.  Fokus ligger i att hitta lösningen på ett problem som ett digitalt konsultföretag står inför som användningsfall. Det digitala konsultföretaget vill använda en datadriven metod för att förstå effekten av olika försäljningsrelaterade aktiviteter i företaget på de försäljningsavtal som företaget stänger. Lösningen innebar att inrama problemet som ett tidsserieprognosproblem för att förutsäga försäljningsavtalen och tolka den underliggande prognosmodellen. Tolkningsförmågan uppnåddes med hjälp av två nya tekniker för agnostisk tolkbarhet, lokala tolkbara modellagnostiska förklaringar (LIME) och Shapley additiva förklaringar (SHAP). Förklaringarna som producerats efter att ha uppnått tolkbarhet utvärderades med hjälp av mänsklig utvärdering av tolkbarhet. Resultaten av de mänskliga utvärderingsstudierna visar tydligt att de förklaringar som produceras av LIME och SHAP starkt hjälpte människor att förstå förutsägelserna från maskininlärningsmodellen. De mänskliga utvärderingsstudieresultaten visade också att LIME- och SHAP-förklaringar var nästan lika förståeliga med LIME som presterade bättre men med en mycket liten marginal. Arbetet som utförts under detta projekt kan enkelt utvidgas till alla tidsserieprognoser eller klassificeringsscenarier för att uppnå och utvärdera tolkbarhet. Dessutom kan detta arbete erbjuda en mycket bra ram för att uppnå och utvärdera tolkbarhet i alla maskininlärningsbaserade regressions- eller klassificeringsproblem.
6

Bäärnhielm, Arvid. "Multiple time-series forecasting on mobile network data using an RNN-RBM model." Thesis, Uppsala universitet, Datalogi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-315782.

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The purpose of this project is to evaluate the performance of a forecasting model based on a multivariate dataset consisting of time series of traffic characteristic performance data from a mobile network. The forecasting is made using machine learning with a deep neural network. The first part of the project involves the adaption of the model design to fit the dataset and is followed by a number of simulations where the aim is to tune the parameters of the model to give the best performance. The simulations show that with well tuned parameters, the neural network performes better than the baseline model, even when using only a univariate dataset. If a multivariate dataset is used, the neural network outperforms the baseline model even when the dataset is small.
7

Noureldin, Diaa. "Essays on multivariate volatility and dependence models for financial time series." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:fdf82d35-a5e7-4295-b7bf-c7009cad7b56.

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This thesis investigates the modelling and forecasting of multivariate volatility and dependence in financial time series. The first paper proposes a new model for forecasting changes in the term structure (TS) of interest rates. Using the level, slope and curvature factors of the dynamic Nelson-Siegel model, we build a time-varying copula model for the factor dynamics allowing for departure from the normality assumption typically adopted in TS models. To induce relative immunity to structural breaks, we model and forecast the factor changes and not the factor levels. Using US Treasury yields for the period 1986:3-2010:12, our in-sample analysis indicates model stability and we show statistically significant gains due to allowing for a time-varying dependence structure which permits joint extreme factor movements. Our out-of-sample analysis indicates the model's superior ability to forecast the conditional mean in terms of root mean square error reductions and directional forecast accuracy. The forecast gains are stronger during the recent financial crisis. We also conduct out-of-sample model evaluation based on conditional density forecasts. The second paper introduces a new class of multivariate volatility models that utilizes high-frequency data. We discuss the models' dynamics and highlight their differences from multivariate GARCH models. We also discuss their covariance targeting specification and provide closed-form formulas for multi-step forecasts. Estimation and inference strategies are outlined. Empirical results suggest that the HEAVY model outperforms the multivariate GARCH model out-of-sample, with the gains being particularly significant at short forecast horizons. Forecast gains are obtained for both forecast variances and correlations. The third paper introduces a new class of multivariate volatility models which is easy to estimate using covariance targeting. The key idea is to rotate the returns and then fit them using a BEKK model for the conditional covariance with the identity matrix as the covariance target. The extension to DCC type models is given, enriching this class. We focus primarily on diagonal BEKK and DCC models, and a related parameterisation which imposes common persistence on all elements of the conditional covariance matrix. Inference for these models is computationally attractive, and the asymptotics is standard. The techniques are illustrated using recent data on the S&P 500 ETF and some DJIA stocks, including comparisons to the related orthogonal GARCH models.
8

Schwartz, Michael. "Optimized Forecasting of Dominant U.S. Stock Market Equities Using Univariate and Multivariate Time Series Analysis Methods." Chapman University Digital Commons, 2017. http://digitalcommons.chapman.edu/comp_science_theses/3.

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This dissertation documents an investigation into forecasting U.S. stock market equities via two very different time series analysis techniques: 1) autoregressive integrated moving average (ARIMA), and 2) singular spectrum analysis (SSA). Approximately 40% of the S&P 500 stocks are analyzed. Forecasts are generated for one and five days ahead using daily closing prices. Univariate and multivariate structures are applied and results are compared. One objective is to explore the hypothesis that a multivariate model produces superior performance over a univariate configuration. Another objective is to compare the forecasting performance of ARIMA to SSA, as SSA is a relatively recent development and has shown much potential. Stochastic characteristics of stock market data are analyzed and found to be definitely not Gaussian, but instead better fit to a generalized t-distribution. Probability distribution models are validated with goodness-of-fit tests. For analysis, stock data is segmented into non-overlapping time “windows” to support unconditional statistical evaluation. Univariate and multivariate ARIMA and SSA time series models are evaluated for independence. ARIMA models are found to be independent, but SSA models are not able to reach independence. Statistics for out-of-sample forecasts are computed for every stock in every window, and multivariate-univariate confidence interval shrinkages are examined. Results are compared for univariate, bivariate, and trivariate combinations of highly-correlated stocks. Effects are found to be mixed. Bivariate modeling and forecasting with three different covariates are investigated. Examination of results with covariates of trading volume, principal component analysis (PCA), and volatility reveal that PCA exhibits the best overall forecasting accuracy in the entire field of investigated elements, including univariate models. Bivariate-PCA structures are applied in a back-testing environment to evaluate economic significance and robustness of the methods. Initial results of back-testing yielded similar results to those from earlier independent testing. Inconsistent performance across test intervals inspired the development of a second technique that yields improved results and positive economic significance. Robustness is validated through back-testing across multiple market trends.
9

Costantini, Mauro, Cuaresma Jesus Crespo, and Jaroslava Hlouskova. "Can Macroeconomists Get Rich Forecasting Exchange Rates?" WU Vienna University of Economics and Business, 2014. http://epub.wu.ac.at/4181/1/wp176.pdf.

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We provide a systematic comparison of the out-of-sample forecasts based on multivariate macroeconomic models and forecast combinations for the euro against the US dollar, the British pound, the Swiss franc and the Japanese yen. We use profit maximization measures based on directional accuracy and trading strategies in addition to standard loss minimization measures. When comparing predictive accuracy and profit measures, data snooping bias free tests are used. The results indicate that forecast combinations help to improve over benchmark trading strategies for the exchange rate against the US dollar and the British pound, although the excess return per unit of deviation is limited. For the euro against the Swiss franc or the Japanese yen, no evidence of generalized improvement in profit measures over the benchmark is found. (authors' abstract)
Series: Department of Economics Working Paper Series
10

Oscar, Nordström. "Multivariate Short-term Electricity Load Forecasting with Deep Learning and exogenous covariates." Thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-183982.

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Maintaining the electricity balance between supply and demand is a challenge for electricity suppliers. If there is an under or overproduction, it entails financial costs and affects consumers and the climate. To better understand how to maintain the balance, can the suppliers use short-term forecasts of electricity load. Hence it is of paramount importance that the forecasts are reliable and of high accuracy. Studies show that time series modeling moves towards more data-driven methods, such as Artificial Neural Networks due to their ability to extract complex relationships and flexibility. This study evaluates the performance of a multivariate Deep Autoregressive Neural Network (DeepAR) in ashort-term forecasting scenario of electricity load, with forecasted weather parameters as exogenous covariates. This thesis’s goal is twofold: to test the performance in terms of evaluation metrics of day-ahead forecasts in exogenous covariates’ presence and examine the robustness when exposing DeepAR to deviations in input data. We perform feature selection on given covariates to identify and extract relevant parameters to facilitate the training process and implement a feature importance algorithm to examine which parameters the model considers essential. To test the robustness, we simulate two cases. In the first case, we introduce Quarantine periods, which mask data prior to the forecast range, and the second case introduces an artificial outlier. An exploratory analysis displays significant annual characteristic differences between seasons, therefore do we use two test sets, one in winter and one in summer. The result shows that DeepAR is robust against potential deviations in input data and that DeepAR surpassed both benchmark models in all of the tested scenarios. In the ideal test scenario where weather parametershad the most significant impact (winter), do DeepAR achieve a Normalized Deviation(ND) of 2.5%, compared to the second-best model, with an ND of 4.4%
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Книги з теми "Multivariate time series forecasting":

1

Zahan, Rifat. Multivariate Time Series: Temperature Forecasting. Dhaka, Bangladesh: VDM Verlag Dr. Müller, 2011.

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2

Kleinbaum, Robert M. Multivariate time series forecasts of market share. Cambridge, Mass: Marketing Science Institute, 1988.

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3

Kleinbaum, Robert M. Multivariate time series forecasts of market share. Cambridge, MA: Marketing Science Institute, 1988.

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4

Harvey, Andrew. Multivariate structural time series model. London: Suntory and ToyotaInternational Centres for Economics and Related Disciplines, 1996.

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5

Chatfield, Christopher. Time-series forecasting. Boca Raton: Chapman & Hall/CRC, 2001.

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6

Reinsel, Gregory C. Elements of multivariate time series analysis. 2nd ed. New York: Springer-Verlag, 1995.

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7

Cromwell, Jeff, Michael Hannan, Walter Labys, and Michel Terraza. Multivariate Tests for Time Series Models. 2455 Teller Road, Thousand Oaks California 91320 United States of America: SAGE Publications, Inc., 1994. http://dx.doi.org/10.4135/9781412985239.

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8

Reinsel, Gregory C. Elements of multivariate time series analysis. 2nd ed. New York: Springer, 1997.

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9

Reinsel, Gregory C. Elements of Multivariate Time Series Analysis. New York, NY: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4684-0198-1.

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10

Reinsel, Gregory C. Elements of Multivariate Time Series Analysis. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-0679-8.

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Частини книг з теми "Multivariate time series forecasting":

1

Moosa, Imad A. "Multivariate Time Series Models." In Exchange Rate Forecasting, 98–133. London: Palgrave Macmillan UK, 2000. http://dx.doi.org/10.1057/9780230379008_4.

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2

Brockwell, Peter J., and Richard A. Davis. "Multivariate Time Series." In Introduction to Time Series and Forecasting, 227–57. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29854-2_8.

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3

Brockwell, Peter J., and Richard A. Davis. "Multivariate Time Series." In Introduction to Time Series and Forecasting, 217–50. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-2526-1_7.

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4

Huang, Changquan, and Alla Petukhina. "Multivariate Time Series Analysis." In Applied Time Series Analysis and Forecasting with Python, 215–56. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13584-2_7.

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5

Cao, Liangyue. "Nonlinear Modelling and Prediction of Multivariate Financial Time Series." In Modelling and Forecasting Financial Data, 199–211. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0931-8_10.

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6

Peña, Mauricio, Argimiro Arratia, and Lluís A. Belanche. "Multivariate Dynamic Kernels for Financial Time Series Forecasting." In Artificial Neural Networks and Machine Learning – ICANN 2016, 336–44. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44781-0_40.

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7

Bhanja, Samit, and Abhishek Das. "Deep Neural Network for Multivariate Time-Series Forecasting." In Advances in Intelligent Systems and Computing, 267–77. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7834-2_25.

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8

Wu, Berlin, and Yu-Yun Hsu. "On Multivariate Fuzzy Time Series Analysis and Forecasting." In Advances in Intelligent and Soft Computing, 363–72. Heidelberg: Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1773-7_38.

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9

Martelo, Sebastián, Diego León, and German Hernandez. "Multivariate Financial Time Series Forecasting with Deep Learning." In Communications in Computer and Information Science, 160–69. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20611-5_14.

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10

Alkhatib, Hamza, Boris Kargoll, and Jens-André Paffenholz. "Further Results on a Robust Multivariate Time Series Analysis in Nonlinear Models with Autoregressive and t-Distributed Errors." In Time Series Analysis and Forecasting, 25–38. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96944-2_3.

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Тези доповідей конференцій з теми "Multivariate time series forecasting":

1

Bae, Juhee, and John Aoga. "Forecasting Migration Intention Using Multivariate Time Series." In ICVISP 2020: 2020 4th International Conference on Vision, Image and Signal Processing. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3448823.3448883.

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2

Wu, 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.

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3

Xinwei Fan and Fengyuan Li. "Multivariate time series forecasting based on BP-SVR." In 2011 International Conference on Computer Science and Service System (CSSS). IEEE, 2011. http://dx.doi.org/10.1109/csss.2011.5974111.

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4

Yin, Jiaming, Weixiong Rao, Mingxuan Yuan, Jia Zeng, Kai Zhao, Chenxi Zhang, Jiangfeng Li, and Qinpei Zhao. "Experimental Study of Multivariate Time Series Forecasting Models." In CIKM '19: The 28th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3357384.3357826.

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5

Pang, Ning, Fengjing Yin, Xiaoyu Zhang, and Xiang Zhao. "A Robust Approach for Multivariate Time Series Forecasting." In SoICT 2017: The Eighth International Symposium on Information and Communication Technology. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3155133.3155172.

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6

Coutinho, Julio Ribeiro, Ricardo Tanscheit, Marley Vellasco, and Adriano Koshiyama. "AutoMFIS: Fuzzy Inference System for multivariate time series forecasting." In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2016. http://dx.doi.org/10.1109/fuzz-ieee.2016.7737953.

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7

Almuammar, 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.

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8

dos Santos, Matheus Cascalho, Frederico Gadelha Guimaraes, and Petronio Candido de Lima e Silva. "High-dimensional Multivariate Time Series Forecasting using Self-Organizing Maps and Fuzzy Time Series." In 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2021. http://dx.doi.org/10.1109/fuzz45933.2021.9494496.

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9

Pan, Qingyi, Wenbo Hu, and Ning Chen. "Two Birds with One Stone: Series Saliency for Accurate and Interpretable Multivariate Time Series Forecasting." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/397.

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It is important yet challenging to perform accurate and interpretable time series forecasting. Though deep learning methods can boost forecasting accuracy, they often sacrifice interpretability. In this paper, we present a new scheme of series saliency to boost both accuracy and interpretability. By extracting series images from sliding windows of the time series, we design series saliency as a mixup strategy with a learnable mask between the series images and their perturbed versions. Series saliency is model agnostic and performs as an adaptive data augmentation method for training deep models. Moreover, by slightly changing the objective, we optimize series saliency to find a mask for interpretable forecasting in both feature and time dimensions. Experimental results on several real datasets demonstrate that series saliency is effective to produce accurate time-series forecasting results as well as generate temporal interpretations.
10

Avazov, Nurilla, Jiamou Liu, and Bakhadyr Khoussainov. "Periodic Neural Networks for Multivariate Time Series Analysis and Forecasting." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8851710.

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Звіти організацій з теми "Multivariate time series forecasting":

1

Anderson, Theodore W. Time Series Analysis and Multivariate Statistical Analysis. Fort Belvoir, VA: Defense Technical Information Center, November 1988. http://dx.doi.org/10.21236/ada202273.

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2

Anderson, Theodore W. Time Series Analysis and Multivariate Statistical Analysis. Fort Belvoir, VA: Defense Technical Information Center, September 1985. http://dx.doi.org/10.21236/ada161375.

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3

McCracken, Michael W., and Tucker McElroy. Multi-Step Ahead Forecasting of Vector Time Series. Federal Reserve Bank of St. Louis, 2012. http://dx.doi.org/10.20955/wp.2012.060.

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4

McDonnell, J. R., and D. E. Waagen. Evolving Cascade-Correlation Networks for Time-Series Forecasting. Fort Belvoir, VA: Defense Technical Information Center, October 1994. http://dx.doi.org/10.21236/ada289197.

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5

Cook, Steve. Employability Skills: Time Series Forecasting at Swansea University. Bristol, UK: The Economics Network, October 2019. http://dx.doi.org/10.53593/n3243a.

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6

Taro Ueki. A Multivariate Time Series Method for Monte Carlo Reactor Analysis. Office of Scientific and Technical Information (OSTI), August 2008. http://dx.doi.org/10.2172/935876.

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7

Gafur, Jamil, and Katherine Candice Kempfert. Forecasting Dengue in Brazil with Time Series Modeling in Parallel. Office of Scientific and Technical Information (OSTI), August 2018. http://dx.doi.org/10.2172/1463575.

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8

Osipov, Gennadij Sergeevich, Natella Semenovna Vashakidze, and Galina Viktorovna Filippova. Basics of forecasting financial time series based on NeuroXL Predictor. Постулат, 2017. http://dx.doi.org/10.18411/postulat-2017-7.

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9

Hafer, R. W. Forecasting Economic Activity: Comparing the Accuracy of Survey and Time Series Predictions. Federal Reserve Bank of St. Louis, 1985. http://dx.doi.org/10.20955/wp.1985.012.

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10

Hafer, R. W., and Scott E. Hein. Forecasting Inflation Using Interest Rate and Time-Series Models: Some International Evidence. Federal Reserve Bank of St. Louis, 1988. http://dx.doi.org/10.20955/wp.1988.001.

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