Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting

Shi, Qiquan; Yin, Jiaming; Cai, Jiajun; Cichocki, Andrzej; Yokota, Tatsuya; Chen, Lei; Yuan, Mingxuan; Zeng, Jia

doi:10.1609/aaai.v34i04.6032

Cited by 62 publications

(36 citation statements)

References 24 publications

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“…Jing et al [20] represented a multiple temporal sequence as a matrix and generalized the AutoRegressive (AR) model by applying it to the temporal sequence matrix. Shi et al [4] proposed a temporal sequence prediction method for short temporal sequences. This approach first transforms a multiple temporal sequence matrix into a high-dimensional tensor, then trains the ARIMA model with learned core tensors.…”

Section: Related Workmentioning

confidence: 99%

“…By merging the autoregressive model and the moving average model with differencing temporal sequence, ARIMA can provide a more accurate prediction for a nonstationary sequence. However, most existing ARIMA models cannot predict multiple sequences simultaneously, they must forecast sequence by sequence, which leads to high-computational costs [4] .…”

Section: Introductionmentioning

confidence: 99%

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A truncated SVD-based ARIMA model for multiple QoS prediction in mobile edge computing

Yan

Zhang

Zhong

et al. 2022

Tsinghua Sci. Technol.

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In the mobile edge computing environments, Quality of Service (QoS) prediction plays a crucial role in web service recommendation. Because of distinct features of mobile edge computing, i.e., the mobility of users and incomplete historical QoS data, traditional QoS prediction approaches may obtain less accurate results in the mobile edge computing environments. In this paper, we treat the historical QoS values at different time slots as a temporal sequence of QoS matrices. By incorporating the compressed matrices extracted from QoS matrices through truncated Singular Value Decomposition (SVD) with the classical ARIMA model, we extend the ARIMA model to predict multiple QoS values simultaneously and efficiently. Experimental results show that our proposed approach outperforms the other state-of-the-art approaches in accuracy and efficiency.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A truncated SVD-based ARIMA model for multiple QoS prediction in mobile edge computing

Yan

Zhang

Zhong

et al. 2022

Tsinghua Sci. Technol.

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show abstract

“…It can add a feature map of one dimension and extract feature information of two dimensions at the same time. In this paper, 2D-CNN is used for convenience and is represented by CNN [29,30]. Convolutional neural networks include convolutional layers and pooling layers.…”

Section: Two-dimensional Convolutional Neural Networkmentioning

confidence: 99%

An Ensemble Model based on Deep Learning and Data Preprocessing for Short-Term Electrical Load Forecasting

Shen

Deng

et al. 2021

Sustainability

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Electricity load forecasting is one of the hot concerns of the current electricity market, and many forecasting models are proposed to satisfy the market participants’ needs. Most of the models have the shortcomings of large computation or low precision. To address this problem, a novel deep learning and data processing ensemble model called SELNet is proposed. We performed an experiment with this model; the experiment consisted of two parts: data processing and load forecasting. In the data processing part, the autocorrelation function (ACF) was used to analyze the raw data on the electricity load and determine the data to be input into the model. The variational mode decomposition (VMD) algorithm was used to decompose the electricity load raw-data into a set of relatively stable modes named intrinsic mode functions (IMFs). According to the time distribution and time lag determined using the ACF, the input of the model was reshaped into a 24 × 7 × 8 matrix M, where 24, 7, and 8 represent 24 h, 7 days, and 8 IMFs, respectively. In the load forecasting part, a two-dimensional convolutional neural network (2D-CNN) was used to extract features from the matrix M. The improved reshaped layer was used to reshape the extracted features according to the time order. A temporal convolutional network was then employed to learn the reshaped time-series features and combined with the fully connected layer to complete the prediction. Finally, the performance of the model was verified in the Eastern Electricity Market of Texas. To demonstrate the effectiveness of the proposed model data processing and load forecasting, we compared it with the gated recurrent unit (GRU), TCN, VMD-TCN, and VMD-CNN models. The TCN exhibited better performance than the GRU in load forecasting. The mean absolute percentage error (MAPE) of the TCN, which was over 5%, was less than that of the GRU. Following the addition of VMD to the TCN, the basic performance of the model was 2–3%. A comparison between the SELNet model and the VMD-TCN model indicated that the application of a 2D-CNN improves the forecast performance, with only a few samples having an MAPE of over 4%. The model’s prediction effect in each season is discussed, and it was found that the proposed model can achieve high-precision prediction in each season.

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“…As mentioned earlier, Hankelization is an effective approach in signal processing for exploiting local correlations of pixels or elements. In many of time series completion or forecasting algorithms, Hankelization has been used for transforming a lower order signal into a higher order matrix or tensor (Shi et al, 2020). An illustration for Hankelizing a time series has been shown in Figure 2.…”

Section: Block Hankelizationmentioning

confidence: 99%

Image Completion in Embedded Space Using Multistage Tensor Ring Decomposition

Sedighin

Cichocki

2021

Front. Artif. Intell.

Self Cite

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Tensor Completion is an important problem in big data processing. Usually, data acquired from different aspects of a multimodal phenomenon or different sensors are incomplete due to different reasons such as noise, low sampling rate or human mistake. In this situation, recovering the missing or uncertain elements of the incomplete dataset is an important step for efficient data processing. In this paper, a new completion approach using Tensor Ring (TR) decomposition in the embedded space has been proposed. In the proposed approach, the incomplete data tensor is first transformed into a higher order tensor using the block Hankelization method. Then the higher order tensor is completed using TR decomposition with rank incremental and multistage strategy. Simulation results show the effectiveness of the proposed approach compared to the state of the art completion algorithms, especially for very high missing ratios and noisy cases.

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Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting

Cited by 62 publications

References 24 publications

A truncated SVD-based ARIMA model for multiple QoS prediction in mobile edge computing

A truncated SVD-based ARIMA model for multiple QoS prediction in mobile edge computing

An Ensemble Model based on Deep Learning and Data Preprocessing for Short-Term Electrical Load Forecasting

Image Completion in Embedded Space Using Multistage Tensor Ring Decomposition

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