Relevant bibliographies by topics / Multivariate time series forecasting

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  1. Journal articles

Academic literature on the topic 'Multivariate time series forecasting'

Author: Grafiati
Published: 10 May 2022
Last updated: 12 June 2025

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Journal articles on the topic "Multivariate time series forecasting"

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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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 (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 (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.
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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 (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 (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 (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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