Relevant bibliographies by topics / Multivariate time series forecasting

Contents

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

Academic literature on the topic 'Multivariate time series forecasting'

Author: Grafiati
Published: 10 May 2022
Last updated: 25 July 2025

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Journal articles on the topic "Multivariate time series forecasting"

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

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

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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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Wu, Haixiang. "Revisiting Attention for Multivariate Time Series Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 20 (2025): 21528–35. https://doi.org/10.1609/aaai.v39i20.35455.

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Current Transformer methods for Multivariate Time-Series Forecasting (MTSF) are all based on the conventional attention mechanism. They involve sequence embedding and performing a linear projection for Q, K, and V, and then computing attention within this latent space. We have not yet delved into the attention mechanism to explore whether such a mapping space is optimal for MTSF. To investigate this issue, we first propose Frequency Spectrum attention (FSatten), a novel attention mechanism based on the frequency domain space. It employs the Fourier transform for embedding and introduces Multi-
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Pei, Jinglei, Yang Zhang, Ting Liu, Jingbin Yang, Qinghua Wu, and Kang Qin. "ADTime: Adaptive Multivariate Time Series Forecasting Using LLMs." Machine Learning and Knowledge Extraction 7, no. 2 (2025): 35. https://doi.org/10.3390/make7020035.

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Large language models (LLMs) have recently demonstrated notable performance, particularly in addressing the challenge of extensive data requirements when training traditional forecasting models. However, these methods encounter significant challenges when applied to high-dimensional and domain-specific datasets. These challenges primarily arise from inability to effectively model inter-variable dependencies and capture variable-specific characteristics, leading to suboptimal performance in complex forecasting scenarios. To address these limitations, we propose ADTime, an adaptive LLM-based app
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Shibo, Feng, Peilin Zhao, Liu Liu, Pengcheng Wu, and Zhiqi Shen. "HDT: Hierarchical Discrete Transformer for Multivariate Time Series Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 1 (2025): 746–54. https://doi.org/10.1609/aaai.v39i1.32057.

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Generative models have gained significant attention in multivariate time series forecasting (MTS), particularly due to their ability to generate high-fidelity samples. Forecasting the probability distribution of multivariate time series is a challenging yet practical task. Although some recent attempts have been made to handle this task, two major challenges persist: 1) some existing generative methods underperform in high-dimensional multivariate time series forecasting, which is hard to scale to higher dimensions; 2) The inherent high-dimensional multivariate attributes constrain the forecas
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Aria, Seyed Sina, Seyed Hossein Iranmanesh, and Hossein Hassani. "Optimizing Multivariate Time Series Forecasting with Data Augmentation." Journal of Risk and Financial Management 17, no. 11 (2024): 485. http://dx.doi.org/10.3390/jrfm17110485.

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The convergence of data mining and deep learning has become an invaluable tool for gaining insights into evolving events and trends. However, a persistent challenge in utilizing these techniques for forecasting lies in the limited access to comprehensive, error-free data. This challenge is particularly pronounced in financial time series datasets, which are known for their volatility. To address this issue, a novel approach to data augmentation has been introduced, specifically tailored for financial time series forecasting. This approach leverages the power of Generative Adversarial Networks
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Feng, Shibo, Chunyan Miao, Zhong Zhang, and Peilin Zhao. "Latent Diffusion Transformer for Probabilistic Time Series Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (2024): 11979–87. http://dx.doi.org/10.1609/aaai.v38i11.29085.

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The probability prediction of multivariate time series is a notoriously challenging but practical task. This research proposes to condense high-dimensional multivariate time series forecasting into a problem of latent space time series generation, to improve the expressiveness of each timestamp and make forecasting more manageable. To solve the problem that the existing work is hard to extend to high-dimensional multivariate time series, we present a latent multivariate time series diffusion framework called Latent Diffusion Transformer (LDT), which consists of a symmetric statistics-aware aut
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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-dimens
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Taga, Ege Onur, Muhammed Emrullah Ildiz, and Samet Oymak. "TimePFN: Effective Multivariate Time Series Forecasting with Synthetic Data." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 19 (2025): 20761–69. https://doi.org/10.1609/aaai.v39i19.34288.

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The diversity of time series applications and scarcity of domain-specific data highlight the need for time-series models with strong few-shot learning capabilities. In this work, we propose a novel training scheme and a transformer-based architecture, collectively referred to as TimePFN, for multivariate time-series (MTS) forecasting. TimePFN is based on the concept of Prior-data Fitted Networks (PFN), which aims to approximate Bayesian inference. Our approach consists of (1) generating synthetic MTS data through diverse Gaussian process kernels and the linear coregionalization method, and (2)
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Dissertations / Theses on the topic "Multivariate time series forecasting"

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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)].
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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
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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<br>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 foreca
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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 predictin
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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
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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 bas
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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
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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
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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 U
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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. T
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Books on the topic "Multivariate time series forecasting"

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

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

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

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Sharer, Elizabeth, and Hari Rajagopalan. Time Series Forecasting. SAGE Publications, Inc., 2023. http://dx.doi.org/10.4135/9781071910269.

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

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Cromwell, Jeff, Michael Hannan, Walter Labys, and Michel Terraza. Multivariate Tests for Time Series Models. SAGE Publications, Inc., 1994. http://dx.doi.org/10.4135/9781412985239.

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

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

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Wei, William W. S. Multivariate Time Series Analysis and Applications. John Wiley & Sons, Ltd, 2019. http://dx.doi.org/10.1002/9781119502951.

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

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Book chapters on the topic "Multivariate time series forecasting"

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

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

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

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

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Saadallah, Amal, Hanna Mykula, and Katharina Morik. "Online Adaptive Multivariate Time Series Forecasting." In Machine Learning and Knowledge Discovery in Databases. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26422-1_2.

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Shahandashti, Mohsen, Bahram Abediniangerabi, Ehsan Zahed, and Sooin Kim. "Construction Time Series Forecasting Using Multivariate Time Series Models." In Construction Analytics. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27292-9_4.

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Levy, Bruno P. C., and Hedibert F. Lopes. "Dynamic Ordering Learning in Multivariate Forecasting." In Time Series and Wavelet Analysis. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-66398-7_5.

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

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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. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44781-0_40.

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

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Conference papers on the topic "Multivariate time series forecasting"

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Liu, Hao, Feng Hu, Zuqiang Su, Jin Dai, Hong Yu, and Yulong Zhou. "WSSNet: A Multivariate Time Series Forecasting Framework Incorporating Serial Correlations." In 2024 China Automation Congress (CAC). IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10865509.

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Hu, Jinhang, and Shijie Li. "INVNET Inverted TimesNet for Multivariate Time Series Forecasting." In 2024 IEEE International Conference on Smart City (SmartCity). IEEE, 2024. https://doi.org/10.1109/smartcity64275.2024.00020.

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Naik, Archana, and Kavitha Sooda. "Multivariate Time Series Forecasting for Cloud Machine Utilization." In 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, 2025. https://doi.org/10.1109/idciot64235.2025.10915007.

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Liu, Yun, and Jinhong Li. "FAformer: Frequency Analysis Transformer for Multivariate Time Series Forecasting." In 2024 IEEE 7th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2024. http://dx.doi.org/10.1109/itnec60942.2024.10733231.

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Liu, Yipu, Zheng Wang, and Qinghua Hu. "Influence-Based Channel Reweighting for Multivariate Time Series Forecasting." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889925.

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Dong, Fanxuan, and Zhuang-Qiang Wu. "Multivariate Meteorological Time Series Forecasting with Transformer-based Models." In 2024 3rd International Conference on Cloud Computing, Big Data Application and Software Engineering (CBASE). IEEE, 2024. https://doi.org/10.1109/cbase64041.2024.10824385.

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Tianyou, Yang, Li Xunbo, Liu Hao, Shi Jieling, Wang Tengfei, and Wang Zhenlin. "Weformer: Wavelet Embedding Transformer for Multivariate Time Series Forecasting." In 2024 21st International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2024. https://doi.org/10.1109/iccwamtip64812.2024.10873624.

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Idnani, Drishti. "A Correlation-Driven Framework for Multivariate Time Series Forecasting." In 2025 International Conference on Engineering, Technology & Management (ICETM). IEEE, 2025. https://doi.org/10.1109/icetm63734.2025.11051536.

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Luo, Yi, Baoyi Wang, Yutong Luo, Ruijuan Yin, Yueyang Wang, and Qingyu Xiong. "Time-Decay Dynamic Graph Neural Network for Multivariate Time Series Forecasting." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651177.

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Hou, Yuntian, and Di Zhang. "Graph Neural Network-Enhanced Multivariate Time Series Forecasting with Series-Core Fusion." In 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE). IEEE, 2025. https://doi.org/10.1109/icaace65325.2025.11019045.

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Reports on the topic "Multivariate time series forecasting"

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

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

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Fisher, Andmorgan, Taylor Hodgdon, and Michael Lewis. Time-series forecasting methods : a review. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49450.

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Time-series forecasting techniques are of fundamental importance for predicting future values by analyzing past trends. The techniques assume that future trends will be similar to historical trends. Forecasting involves using models fit on historical data to predict future values. Time-series models have wide-ranging applications, from weather forecasting to sales forecasting, and are among the most effective methods of forecasting, especially when making decisions that involve uncertainty about the future. To evaluate forecast accuracy and to compare among models fitted to a time series, thre
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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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McDonnell, J. R., and D. E. Waagen. Evolving Cascade-Correlation Networks for Time-Series Forecasting. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada289197.

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

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Prasad, Jayanti. Time Series Analysis & Forecasting: Foundations and Applications. Instats Inc., 2024. http://dx.doi.org/10.61700/hxyoh0fib1cd8853.

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This 5-day workshop offers a comprehensive exploration of time series analysis and forecasting, equipping participants with the skills to apply these techniques in their own research across various disciplines. The seminar provides hands-on Python experience, advanced techniques, case studies, and a deep understanding of the principles and applications of time series analysis. An official Instats certificate of completion and ECTS Equivalent points are provided at the conclusion of the seminar.
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Taro Ueki. A Multivariate Time Series Method for Monte Carlo Reactor Analysis. Office of Scientific and Technical Information (OSTI), 2008. http://dx.doi.org/10.2172/935876.

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

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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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