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Journal articles on the topic 'Multivariate time series forecasting'

Author: Grafiati

Published: 10 May 2022

Last updated: 25 July 2025

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1

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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2

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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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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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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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 (2023): 141–49. http://dx.doi.org/10.26599/tst.2021.9010081.

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Shapovalova, Yuliya, Nalan Baştürk, and Michael Eichler. "Multivariate Count Data Models for Time Series Forecasting." Entropy 23, no. 6 (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

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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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14

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

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15

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

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

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17

Wang, Xuguang, Mi Zhang, and Jie Su. "Robust temporal alignment for multivariate time series forecasting." Expert Systems with Applications 289 (September 2025): 128299. https://doi.org/10.1016/j.eswa.2025.128299.

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18

Wan, Renzhuo, Chengde Tian, Wei Zhang, Wendi Deng, and Fan Yang. "A Multivariate Temporal Convolutional Attention Network for Time-Series Forecasting." Electronics 11, no. 10 (2022): 1516. http://dx.doi.org/10.3390/electronics11101516.

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Multivariate time-series forecasting is one of the crucial and persistent challenges in time-series forecasting tasks. As a kind of data with multivariate correlation and volatility, multivariate time series impose highly nonlinear time characteristics on the forecasting model. In this paper, a new multivariate time-series forecasting model, multivariate temporal convolutional attention network (MTCAN), based on a self-attentive mechanism is proposed. MTCAN is based on the Convolution Neural Network (CNN) model, using 1D dilated convolution as the basic unit to construct asymmetric blocks, and

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19

Kang, Seung Woo, and Ohyun Jo. "Multivariate Time-Series Imagification with Time Embedding in Constrained Environments (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23535–36. http://dx.doi.org/10.1609/aaai.v38i21.30461.

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We present an imagification approach for multivariate time-series data tailored to constrained NN-based forecasting model training environments. Our imagification process consists of two key steps: Re-stacking and time embedding. In the Re-stacking stage, time-series data are arranged based on high correlation, forming the first image channel using a sliding window technique. The time embedding stage adds two additional image channels by incorporating real-time information. We evaluate our method by comparing it with three benchmark imagification techniques using a simple CNN-based model. Addi

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20

Díaz Berenguer, Abel, Yifei Da, Matías Nicolás Bossa, Meshia Cédric Oveneke, and Hichem Sahli. "Causality-driven multivariate stock movement forecasting." PLOS ONE 19, no. 4 (2024): e0302197. http://dx.doi.org/10.1371/journal.pone.0302197.

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Our study aims to investigate the interdependence between international stock markets and sentiments from financial news in stock forecasting. We adopt the Temporal Fusion Transformers (TFT) to incorporate intra and inter-market correlations and the interaction between the information flow, i.e. causality, of financial news sentiment and the dynamics of the stock market. The current study distinguishes itself from existing research by adopting Dynamic Transfer Entropy (DTE) to establish an accurate information flow propagation between stock and sentiments. DTE has the advantage of providing ti

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21

Ahmadi, Ahmadi, and R. Adisetiawan. "Multivariate Time Series in Macroeconomics." Eksis: Jurnal Ilmiah Ekonomi dan Bisnis 11, no. 2 (2020): 151. http://dx.doi.org/10.33087/eksis.v11i2.209.

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Gold is one of the most popular commodities and investment alternatives. Gold prices are thought to be influenced by several other factors such as the US Dollar, oil price, inflation rate, and stock exchange so that gold price modeling is not only influenced by its own value. This research was conducted to determine the best forecasting model and to find out what factors influence the price of gold. This research modeled the price of gold in a multivariate and reviewed the univariate modeling that will be used as a comparison model of multivariate modeling. Univariate modeling is done using AR

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22

He, Zichao, Chunna Zhao, and Yaqun Huang. "Multivariate Time Series Deep Spatiotemporal Forecasting with Graph Neural Network." Applied Sciences 12, no. 11 (2022): 5731. http://dx.doi.org/10.3390/app12115731.

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Multivariate time series forecasting has long been a subject of great concern. For example, there are many valuable applications in forecasting electricity consumption, solar power generation, traffic congestion, finance, and so on. Accurately forecasting periodic data such as electricity can greatly improve the reliability of forecasting tasks in engineering applications. Time series forecasting problems are often modeled using deep learning methods. However, the deep information of sequences and dependencies among multiple variables are not fully utilized in existing methods. Therefore, a mu

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23

Calderon, Sergio, and Fabio H. Nieto. "Forecasting with Multivariate Threshold Autoregressive Models." Revista Colombiana de Estadística 44, no. 2 (2021): 369–83. http://dx.doi.org/10.15446/rce.v44n2.91356.

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   An important stage in the analysis of time series is the forecasting. How- ever, the forecasting in non-linear time series models is not straightforward as in linear time series models because an exact analytical of the conditional expectation is not easy to obtain. Therefore, a strategy of forecasting with multivariate threshold autoregressive(MTAR) models is proposed via predictive distributions through Bayesian approach. This strategy gives us the forecast for the response and exogenous vectors. The coverage percentages of the forecast intervals and the variability of t

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24

Lee, Won Kyung. "Partial Correlation-Based Attention for Multivariate Time Series Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (2020): 13720–21. http://dx.doi.org/10.1609/aaai.v34i10.7132.

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A multivariate time-series forecasting has great potentials in various domains. However, it is challenging to find dependency structure among the time-series variables and appropriate time-lags for each variable, which change dynamically over time. In this study, I suggest partial correlation-based attention mechanism which overcomes the shortcomings of existing pair-wise comparisons-based attention mechanisms. Moreover, I propose data-driven series-wise multi-resolution convolutional layers to represent the input time-series data for domain agnostic learning.

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25

Cheng, Yunyao, Peng Chen, Chenjuan Guo, et al. "Weakly Guided Adaptation for Robust Time Series Forecasting." Proceedings of the VLDB Endowment 17, no. 4 (2023): 766–79. http://dx.doi.org/10.14778/3636218.3636231.

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Robust multivariate time series forecasting is crucial in many cyberphysical and Internet of Things applications. Existing state-of-the-art robust forecasting models decompose time series into independent functions covering trends and periodicities. However, these independent functions fail to capture correlations among multiple time series, thereby reducing prediction accuracy. Moreover, existing robust forecasting models treat certain abrupt but normal changes, e.g., caused by holidays, as outliers because they occur infrequently and have data distributions that resemble those of outliers. T

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26

Sobanapuram Muruganandam, Narendran, and Umamakeswari Arumugam. "Dynamic Ensemble Multivariate Time Series Forecasting Model for PM2.5." Computer Systems Science and Engineering 44, no. 2 (2023): 979–89. http://dx.doi.org/10.32604/csse.2023.024943.

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27

Yazdanbakhsh, Omolbanin, and Scott Dick. "Forecasting of Multivariate Time Series via Complex Fuzzy Logic." IEEE Transactions on Systems, Man, and Cybernetics: Systems 47, no. 8 (2017): 2160–71. http://dx.doi.org/10.1109/tsmc.2016.2630668.

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Kun-Huang Huarng, Tiffany Hui-Kuang Yu, and Yu Wei Hsu. "A Multivariate Heuristic Model for Fuzzy Time-Series Forecasting." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 37, no. 4 (2007): 836–46. http://dx.doi.org/10.1109/tsmcb.2006.890303.

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29

Ng, C. N., P. C. Young, and C. Wang. "Recursive Identification. Estimation and Forecasting of Multivariate Time-series." IFAC Proceedings Volumes 21, no. 9 (1988): 593–96. http://dx.doi.org/10.1016/s1474-6670(17)54792-8.

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30

Li, Zhuo Lin, Gao Wei Zhang, Jie Yu, and Ling Yu Xu. "Dynamic graph structure learning for multivariate time series forecasting." Pattern Recognition 138 (June 2023): 109423. http://dx.doi.org/10.1016/j.patcog.2023.109423.

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31

Wang, Yulong, Yushuo Liu, Xiaoyi Duan, and Kai Wang. "FilterTS: Comprehensive Frequency Filtering for Multivariate Time Series Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 20 (2025): 21375–83. https://doi.org/10.1609/aaai.v39i20.35438.

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Multivariate time series forecasting is crucial across various industries, where accurate extraction of complex periodic and trend components can significantly enhance prediction performance. However, existing models often struggle to capture these intricate patterns. To address these challenges, we propose FilterTS, a novel forecasting model that utilizes specialized filtering techniques based on the frequency domain. FilterTS introduces a Dynamic Cross-Variable Filtering Module, a key innovation that dynamically leverages other variables as filters to extract and reinforce shared variable fr

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Yu, Yongbo, Weizhong Yu, Feiping Nie, Zongcheng Miao, Ya Liu, and Xuelong Li. "PRformer: Pyramidal recurrent transformer for multivariate time series forecasting." Neural Networks 191 (November 2025): 107769. https://doi.org/10.1016/j.neunet.2025.107769.

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Rasheed, Abdul, Muhammad Asad Ullah, and Imam Uddin. "PKR Exchange Rate Forecasting Through Univariate and Multivariate Time Series Techniques." NICE Research Journal 13, no. 4 (2020): 49–67. http://dx.doi.org/10.51239/nrjss.v13i4.226.

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This study aims to examine and compare the accuracy of time series and econometric forecasting models in the context of the exchange rate as we know that fluctuation in the exchange rate may affect the economic activities at the macro – level. For this purpose, the author has chosen the Pakistani Rupee exchange rate against United States Dollars with the annual data from 1980 to 2018. The results revealed that the exponential model provides the most effective accuracy in forecasting rather than the Naive, ARIMA and ARDL Co-integration model. This paper has also covered the gap of unavailabilit

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Bhanja, Samit, Banani Ghose, and Abhishek Das. "Multi-Step-Ahead Time Series Forecasting using Deep Learning and Fuzzy Time Series-based Error Correction Method." JUCS - Journal of Universal Computer Science 30, no. (11) (2024): 1569–94. https://doi.org/10.3897/jucs.114357.

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Recently time series forecasting has become one of the prime application areas of climatology, economics and industries. Many research works are conducted to forecast the time series more accurately. But few of them are concentrated on predicting the time series over an extended future horizon, and there is also a scope to improve their forecasting accuracy. This work proposes a multi-step-ahead foresting method to produce a stable and accurate forecasting result for the extended future horizon. Firstly, a deep learning-based forecasting model is proposed to predict the time series. Secondly,

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Yu, Yue, Pavel Loskot, Wenbin Zhang, Qi Zhang, and Yu Gao. "A Spatial–Temporal Time Series Decomposition for Improving Independent Channel Forecasting." Mathematics 13, no. 14 (2025): 2221. https://doi.org/10.3390/math13142221.

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Forecasting multivariate time series is a pivotal task in controlling multi-sensor systems. The joint forecasting of all channels may be too complex, whereas forecasting the channels independently may cause important spatial inter-dependencies to be overlooked. In this paper, we improve the performance of single-channel forecasting algorithms by designing an interpretable front-end that extracts the spatial–temporal components from the input multivariate time series. Specifically, the multivariate samples are first segmented into equal-sized matrix symbols. The symbols are decomposed into the

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Zhao, Mengmeng, Haipeng Peng, Lixiang Li, and Yeqing Ren. "Graph Attention Network and Informer for Multivariate Time Series Anomaly Detection." Sensors 24, no. 5 (2024): 1522. http://dx.doi.org/10.3390/s24051522.

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Time series anomaly detection is very important to ensure the security of industrial control systems (ICSs). Many algorithms have performed well in anomaly detection. However, the performance of most of these algorithms decreases sharply with the increase in feature dimension. This paper proposes an anomaly detection scheme based on Graph Attention Network (GAT) and Informer. GAT learns sequential characteristics effectively, and Informer performs excellently in long time series prediction. In addition, long-time forecasting loss and short-time forecasting loss are used to detect multivariate

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Feng, Xing, Hongru Li, and Yinghua Yang. "Time-lagged relation graph neural network for multivariate time series forecasting." Engineering Applications of Artificial Intelligence 139 (January 2025): 109530. http://dx.doi.org/10.1016/j.engappai.2024.109530.

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Li, ZhuoLin, ZiHeng Gao, XiaoLin Zhang, GaoWei Zhang, and LingYu Xu. "Time-aware personalized graph convolutional network for multivariate time series forecasting." Expert Systems with Applications 240 (April 2024): 122471. http://dx.doi.org/10.1016/j.eswa.2023.122471.

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Dou, Jiaxin, Yaling Xun, Haifeng Yang, Jianghui Cai, Yanfeng Li, and Shuo Han. "Multivariate time series forecasting based on time–frequency transform mixed convolution." Knowledge-Based Systems 325 (September 2025): 113912. https://doi.org/10.1016/j.knosys.2025.113912.

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Cai, Wanlin, Yuxuan Liang, Xianggen Liu, Jianshuai Feng, and Yuankai Wu. "MSGNet: Learning Multi-Scale Inter-series Correlations for Multivariate Time Series Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (2024): 11141–49. http://dx.doi.org/10.1609/aaai.v38i10.28991.

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Multivariate time series forecasting poses an ongoing challenge across various disciplines. Time series data often exhibit diverse intra-series and inter-series correlations, contributing to intricate and interwoven dependencies that have been the focus of numerous studies. Nevertheless, a significant research gap remains in comprehending the varying inter-series correlations across different time scales among multiple time series, an area that has received limited attention in the literature. To bridge this gap, this paper introduces MSGNet, an advanced deep learning model designed to capture

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Chen, Jason Li, Gang Li, Doris Chenguang Wu, and Shujie Shen. "Forecasting Seasonal Tourism Demand Using a Multiseries Structural Time Series Method." Journal of Travel Research 58, no. 1 (2017): 92–103. http://dx.doi.org/10.1177/0047287517737191.

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Multivariate forecasting methods are intuitively appealing since they are able to capture the interseries dependencies, and therefore may forecast more accurately. This study proposes a multiseries structural time series method based on a novel data restacking technique as an alternative approach to seasonal tourism demand forecasting. The proposed approach is analogous to the multivariate method but only requires one variable. In this study, a quarterly tourism demand series is split into four component series, each component representing the demand in a particular quarter of each year; the c

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Nguyen, Nam, and Brian Quanz. "Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (2021): 9117–25. http://dx.doi.org/10.1609/aaai.v35i10.17101.

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Probabilistic forecasting of high dimensional multivariate time series is a notoriously challenging task, both in terms of computational burden and distribution modeling. Most previous work either makes simple distribution assumptions or abandons modeling cross-series correlations. A promising line of work exploits scalable matrix factorization for latent-space forecasting, but is limited to linear embeddings, unable to model distributions, and not trainable end-to-end when using deep learning forecasting. We introduce a novel temporal latent auto-encoder method which enables nonlinear factori

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Huang, Lei, Feng Mao, Kai Zhang, and Zhiheng Li. "Spatial-Temporal Convolutional Transformer Network for Multivariate Time Series Forecasting." Sensors 22, no. 3 (2022): 841. http://dx.doi.org/10.3390/s22030841.

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Multivariate time series forecasting has long been a research hotspot because of its wide range of application scenarios. However, the dynamics and multiple patterns of spatiotemporal dependencies make this problem challenging. Most existing methods suffer from two major shortcomings: (1) They ignore the local context semantics when modeling temporal dependencies. (2) They lack the ability to capture the spatial dependencies of multiple patterns. To tackle such issues, we propose a novel Transformer-based model for multivariate time series forecasting, called the spatial–temporal convolutional

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Wan, Renzhuo, Shuping Mei, Jun Wang, Min Liu, and Fan Yang. "Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting." Electronics 8, no. 8 (2019): 876. http://dx.doi.org/10.3390/electronics8080876.

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Multivariable time series prediction has been widely studied in power energy, aerology, meteorology, finance, transportation, etc. Traditional modeling methods have complex patterns and are inefficient to capture long-term multivariate dependencies of data for desired forecasting accuracy. To address such concerns, various deep learning models based on Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) methods are proposed. To improve the prediction accuracy and minimize the multivariate time series data dependence for aperiodic data, in this article, Beijing PM2.5 and ISO-N

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Hendi, Hanaa Ghareib, Mohamed Hassan Ibrahim, Masoud Esmail Masoud Shaheen, and Mohamed Hassan Farrag. "Multi-Time Series Forecasting for Regional Emergency Call Demand." International Journal of Healthcare Information Systems and Informatics 20, no. 1 (2025): 1–15. https://doi.org/10.4018/ijhisi.375011.

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Accurate emergency call demand forecasting is essential for optimizing resource allocation and response times in Emergency Medical Services (EMS). Time series forecasting, a cornerstone of machine learning, plays a crucial role in predicting demand patterns. This research proposes a novel multi-series forecasting model for scenarios involving multiple independent time series, representing call data from distinct service areas. While previous research has explored multivariate time series and machine learning methods for EMS demand forecasting, this study focuses on comparing a simplified indep

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Haviluddin, Haviluddin, and Rayner Alfred. "Multi-step CNN forecasting for COVID-19 multivariate time-series." International Journal of Advances in Intelligent Informatics 9, no. 2 (2023): 176. http://dx.doi.org/10.26555/ijain.v9i2.1080.

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The new coronavirus (COVID-19) has spread to over 200 countries, with over 36 million confirmed cases as of October 10, 2020. As a result, numerous machine learning models capable of forecasting the epidemic worldwide have been produced. This paper reviews and summarizes the most relevant machine learning forecasting models for COVID-19. The dataset is derived from the world health organization (WHO) COVID-19 dashboard, and it contains official daily counts of COVID-19 cases, fatalities, and vaccination use reported by countries, territories, and regions. We propose various convolutional neura

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Palaskar, Santosh, Vijay Ekambaram, Arindam Jati, et al. "AutoMixer for Improved Multivariate Time-Series Forecasting on Business and IT Observability Data." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 22962–68. http://dx.doi.org/10.1609/aaai.v38i21.30336.

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The efficiency of business processes relies on business key performance indicators (Biz-KPIs), that can be negatively impacted by IT failures. Business and IT Observability (BizITObs) data fuses both Biz-KPIs and IT event channels together as multivariate time series data. Forecasting Biz-KPIs in advance can enhance efficiency and revenue through proactive corrective measures. However, BizITObs data generally exhibit both useful and noisy inter-channel interactions between Biz-KPIs and IT events that need to be effectively decoupled. This leads to suboptimal forecasting performance when existi

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48

Li, Shaowei, He Huang, and Wei Lu. "A Neural Networks Based Method for Multivariate Time-Series Forecasting." IEEE Access 9 (2021): 63915–24. http://dx.doi.org/10.1109/access.2021.3075063.

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Zhang, Huihui, Shicheng Li, Yu Chen, Jiangyan Dai, and Yugen Yi. "A Novel Encoder-Decoder Model for Multivariate Time Series Forecasting." Computational Intelligence and Neuroscience 2022 (April 14, 2022): 1–17. http://dx.doi.org/10.1155/2022/5596676.

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The time series is a kind of complex structure data, which contains some special characteristics such as high dimension, dynamic, and high noise. Moreover, multivariate time series (MTS) has become a crucial study in data mining. The MTS utilizes the historical data to forecast its variation trend and has turned into one of the hotspots. In the era of rapid information development and big data, accurate prediction of MTS has attracted much attention. In this paper, a novel deep learning architecture based on the encoder-decoder framework is proposed for MTS forecasting. In this architecture, f

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Chiu, Yi Chia, and Joseph Z. Shyu. "Applying multivariate time series models to technological product sales forecasting." International Journal of Technology Management 27, no. 2/3 (2004): 306. http://dx.doi.org/10.1504/ijtm.2004.003957.

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