Modeling the stochastic mechanism of sensor using a hybrid method based on seasonal autoregressive integrated moving average time series and generalized estimating equations

Mahmoudi, Mohammad Reza; Baroumand, Salman

doi:10.1016/j.isatra.2021.07.013

Cited by 12 publications

(3 citation statements)

References 25 publications

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“…The exponential moving average (EMA) [38] addresses several issues associated with SMA, including the fact that SMA assigns equal weight to all data points in a time series. In contrast, EMA assigns different weight to the data points at each time point in the time series.…”

Section: Simple Moving Averagementioning

confidence: 99%

Integrated Energy System Based on Isolation Forest and Dynamic Orbit Multivariate Load Forecasting

Wu,

Ma,

Alharbi

et al. 2023

Sustainability

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Short-term load forecasting is a prerequisite for achieving intra-day energy management and optimal scheduling in integrated energy systems. Its prediction accuracy directly affects the stability and economy of the system during operation. To improve the accuracy of short-term load forecasting, this paper proposes a multi-load forecasting method for integrated energy systems based on the Isolation Forest and dynamic orbit algorithm. First, a high-dimensional data matrix is constructed using the sliding window technique and the outliers in the high-dimensional data matrix are identified using Isolation Forest. Next, the hidden abnormal data within the time series are analyzed and repaired using the dynamic orbit algorithm. Then, the correlation analysis of the multivariate load and its weather data is carried out by the AR method and MIC method, and the high-dimensional feature matrix is constructed. Finally, the prediction values of the multi-load are generated based on the TCN-MMoL multi-task training network. Simulation analysis is conducted using the load data from a specific integrated energy system. The results demonstrate the proposed model’s ability to significantly improve load forecasting accuracy, thereby validating the correctness and effectiveness of this forecasting approach.

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Section: Simple Moving Averagementioning

confidence: 99%

Integrated Energy System Based on Isolation Forest and Dynamic Orbit Multivariate Load Forecasting

Wu,

Ma,

Alharbi

et al. 2023

Sustainability

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show abstract

“…Statistical models are the classical model, employed to predict stable and autocorrelated time series data. Although autoregressive (AR) [ 16 ], moving average (MA) [ 17 ], the autoregressive moving average (ARMA) [ 18 ], and the autoregressive integrated moving average (ARIMA) [ 19 ] have advantages in dealing with the univariate time series, they cannot model the dynamic spatial-temporal correlations of multivariate time series. The vector autoregressive (VAR) [ 20 ] model considers the relationships between stable and autocorrelated time series, and the information is limited.…”

Section: Related Workmentioning

confidence: 99%

A joint network of non-linear graph attention and temporal attraction force for geo-sensory time series prediction

et al. 2022

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Geo-sensory time series, such as the air quality and water distribution, are collected from numerous sensors at different geospatial locations in the same time interval. Each sensor monitors multiple parameters and generates multivariate time series. These time series change over time and vary geographically; hence, geo-sensory time series contain multi-scale spatial-temporal correlations, namely inter-sensor spatial-temporal correlations and intra-sensor spatial-temporal correlations. To capture spatial-temporal correlations, although various deep learning models have been developed, few of the models focus on capturing both correlations. To solve this problem, we propose simultaneously capture the inter- and intra-sensor spatial-temporal correlations by designing a joint network of non-linear graph attention and temporal attraction force(J-NGT) consisting two graph attention mechanisms. The non-linear graph attention mechanism can characterize node affinities for adaptively selecting the relevant exogenous series and relevant sensor series. The temporal attraction force mechanism can weigh the effect of past values on current values to represent the temporal correlation. To prove the superiority and effectiveness of our model, we evaluate our model in three real-world datasets from different fields. Experimental results show that our model can achieve better prediction performance than eight state-of-the-art models, including statistical models, machine learning models, and deep learning models. Furthermore, we conducted experiments to capture inter- and intra-sensor spatial-temporal correlations. Experimental results indicate that our model significantly improves performance by capturing both inter- and intra-sensor spatial-temporal correlations. This fully shows that our model has a greater advantage in geo-sensory time series prediction.

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“…It can only learn the sequence characteristics of the data, while it cannot capture the physical rules of the atmosphere. Traditional mathematical-statistical methods such as autoregression (AR), moving average (MA), and autoregression integrated moving average (ARIMA) cannot capture the spatial distribution information of physical phenomena in the atmosphere when predicting time series [36][37][38][39][40][41]. Similar to TCs and many synoptic-scale vortices, the distribution modality of the polar vortex will change greatly in a few days or even a few hours.…”

Section: Introduction 1concept and Research Backgroundmentioning

confidence: 99%

Polar Vortex Multi-Day Intensity Prediction Relying on New Deep Learning Model: A Combined Convolution Neural Network with Long Short-Term Memory Based on Gaussian Smoothing Method

Peng

Cao

Liu

et al. 2021

Entropy

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The variation of polar vortex intensity is a significant factor affecting the atmospheric conditions and weather in the Northern Hemisphere (NH) and even the world. However, previous studies on the prediction of polar vortex intensity are insufficient. This paper establishes a deep learning (DL) model for multi-day and long-time intensity prediction of the polar vortex. Focusing on the winter period with the strongest polar vortex intensity, geopotential height (GPH) data of NCEP from 1948 to 2020 at 50 hPa are used to construct the dataset of polar vortex anomaly distribution images and polar vortex intensity time series. Then, we propose a new convolution neural network with long short-term memory based on Gaussian smoothing (GSCNN-LSTM) model which can not only accurately predict the variation characteristics of polar vortex intensity from day to day, but also can produce a skillful forecast for lead times of up to 20 days. Moreover, the innovative GSCNN-LSTM model has better stability and skillful correlation prediction than the traditional and some advanced spatiotemporal sequence prediction models. The accuracy of the model suggests important implications that DL methods have good applicability in forecasting the nonlinear system and vortex spatial–temporal characteristics variation in the atmosphere.

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Modeling the stochastic mechanism of sensor using a hybrid method based on seasonal autoregressive integrated moving average time series and generalized estimating equations

Cited by 12 publications

References 25 publications

Integrated Energy System Based on Isolation Forest and Dynamic Orbit Multivariate Load Forecasting

Integrated Energy System Based on Isolation Forest and Dynamic Orbit Multivariate Load Forecasting

A joint network of non-linear graph attention and temporal attraction force for geo-sensory time series prediction

Polar Vortex Multi-Day Intensity Prediction Relying on New Deep Learning Model: A Combined Convolution Neural Network with Long Short-Term Memory Based on Gaussian Smoothing Method

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