Autoregressive–moving-average model ≈ Autoregressive–moving-average model
View article: Graph Neural Networks with Convolutional ARMA Filters
Graph Neural Networks with Convolutional ARMA Filters Open
Popular graph neural networks implement convolution operations on graphs based on polynomial spectral filters. In this paper, we propose a novel graph convolutional layer inspired by the auto-regressive moving average (ARMA) filter that, c…
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Autoregressive Moving Average Graph Filtering Open
One of the cornerstones of the field of signal processing on graphs are graph filters, direct analogues of classical filters, but intended for signals defined on graphs. This work brings forth new insights on the distributed graph filterin…
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Stationary Graph Processes and Spectral Estimation Open
Stationarity is a cornerstone property that facilitates the analysis and processing of random signals in the time domain. Although time-varying signals are abundant in nature, in many practical scenarios the information of interest resides…
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Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting Open
In this paper, the historical power load data from the National Electricity Market (Australia) is used to analyze the characteristics and regulations of electricity (the average value of every eight hours). Then, considering the inverse of…
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SARIMA modelling approach for railway passenger flow forecasting Open
In this paper, railway passenger flows are analyzed and a suitable modeling method proposed. Based on historical data composed from monthly passenger counts realized on Serbian railway network it is concluded that the time series has a str…
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Clustered Hybrid Wind Power Prediction Model Based on ARMA, PSO-SVM, and Clustering Methods Open
Wind power prediction is the key technology to the safe dispatch and stable operation of power system with large-scale integration of wind power. In this work, based on the historical data of wind power, wind speed and temperature, the aut…
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Big Data Analytics and Structural Health Monitoring: A Statistical Pattern Recognition-Based Approach Open
Recent advances in sensor technologies and data acquisition systems opened up the era of big data in the field of structural health monitoring (SHM). Data-driven methods based on statistical pattern recognition provide outstanding opportun…
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Time Series Forecasting of Temperatures using SARIMA: An Example from Nanjing Open
Time series modelling and forecasting – a method that predicts future values by analysing past values - plays an important role in many practical fields. In this paper, we analyse the monthly mean temperature in Nanjing, China, from 1951 t…
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State of Health Estimation for Lithium-ion Batteries Based on Fusion of Autoregressive Moving Average Model and Elman Neural Network Open
This paper proposes a fusion model based on the autoregressive moving average (ARMA) model and Elman neural network (NN) to achieve accurate prediction for the state of health (SOH) of lithium-ion batteries. First, the voltage and capacity…
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Linear Serial Rank Tests for Randomness Against Arma Alternatives Open
In this paper we introduce a class of linear serial rank statistics for the problem of testing white noise against alternatives of ARMA serial dependence. The asymptotic normality of the proposed statistics is established, both under the n…
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Forecasting Time Series With VARMA Recursions on Graphs Open
Graph-based techniques emerged as a choice to deal with the dimensionality\nissues in modeling multivariate time series. However, there is yet no complete\nunderstanding of how the underlying structure could be exploited to ease this\ntask…
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EVDHM-ARIMA-Based Time Series Forecasting Model and Its Application for COVID-19 Cases Open
The time-series forecasting makes a substantial contribution in timely decision-making. In this article, a recently developed eigenvalue decomposition of Hankel matrix (EVDHM) along with the autoregressive integrated moving average (ARIMA)…
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Financial Time Series Forecasting with the Deep Learning Ensemble Model Open
With the continuous development of financial markets worldwide to tackle rapid changes such as climate change and global warming, there has been increasing recognition of the importance of financial time series forecasting in financial mar…
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Wind power prediction based on variational mode decomposition multi-frequency combinations Open
Because of the uncertainty and randomness of wind speed, wind power has characteristics such as nonlinearity and multiple frequencies. Accurate prediction of wind power is one effective means of improving wind power integration. Because th…
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A Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX) Forecasting Model-Based Time Series Approach Open
Time series modeling is an effective approach for studying and analyzing the future performance of the power sector based on historical data. This study proposes a forecasting framework that applies a seasonal autoregressive integrated mov…
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Non-linear Autoregressive with Exogeneous input (narx) bitcoin price prediction model using PSO-optimized parameters and moving average technical indicators Open
This paper presents a Multi-Layer Exogeneous Inputs (NARX) Bitcoin price forecasting model using the opening, closing, minimum and maximum past prices together with Moving Average (MA) technical indicators.As there were many parameter (PSO…
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Model Estimation of ARMA Using Genetic Algorithms: A Case Study of Forecasting Natural Gas Consumption Open
Energy is accepted as a vital strategic issue all over the world due to the important hesitations/concerns about energy reliability, sustainability and affordability. The future of the any country's economy entirely depends on energy becau…
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Univariate time series prediction of solar power using a hybrid wavelet-ARMA-NARX prediction method Open
This paper proposes a new hybrid method for super short-term solar power prediction. Solar output power usually has a complex, nonstationary, and nonlinear characteristic due to intermittent and time varying behavior of solar radiance. In …
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Research on Real-Time Local Rainfall Prediction Based on MEMS Sensors Open
A more accurate and timely rainfall prediction is needed for flood disaster reduction and prevention in Wuhan. The in situ microelectromechanical systems’ (MEMS) sensors can provide high time and spatial resolution of weather parameter mea…
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A Hybrid Forecasting Method for Solar Output Power Based on Variational Mode Decomposition, Deep Belief Networks and Auto-Regressive Moving Average Open
Due to the existing large-scale grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economical operation of electric power systems. In this study, a hybrid short-term f…
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The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling Open
In this research, two hybrid intelligent models are proposed for prediction accuracy enhancement for wind speed and power modeling. The established models are based on the hybridisation of Ensemble Empirical Mode Decomposition (EEMD) with …
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Autoregressive Times Series Methods for Time Domain Astronomy Open
Celestial objects exhibit a wide range of variability in brightness at\ndifferent wavebands. Surprisingly, the most common methods for characterizing\ntime series in statistics -- parametric autoregressive modeling -- is rarely\nused to in…
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Random Forest Based Feature Selection of Macroeconomic Variables for Stock Market Prediction Open
A firm’s equity price on the stock-market is reported to be closely related to the Macroeconomic Variable (MVs) of the country in which the firm trades. For this reason, researchers, market traders, financial analysts and forecasters to ex…
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Data-based stochastic model reduction for the Kuramoto–Sivashinsky equation Open
In this paper, the problem of constructing data-based, predictive, reduced models for the Kuramoto–Sivashinsky equation is considered, under circumstances where one has observation data only for a small subset of the dynamical variables. A…
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Wind Power Forecasting Open
Accurate short-term wind power forecast is very important for reliable and efficient operation of power systems with high wind power penetration. There are many conventional and artificial intelligence methods that have been developed to a…
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Performance analysis of (TDD) massive MIMO with Kalman channel prediction Open
In massive MIMO systems, which rely on uplink pilots to estimate the channel, the time interval between pilot transmissions constrains the length of the downlink. Since switching between up- and downlink takes time, longer downlink blocks …
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Forecasting Network Traffic: A Survey and Tutorial With Open-Source Comparative Evaluation Open
This paper presents a review of the literature on network traffic prediction, while also serving as a tutorial to the topic. We examine works based on autoregressive moving average models, like ARMA, ARIMA and SARIMA, as well as works base…
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A Short-Term Wind Speed Forecasting Model Based on a Multi-Variable Long Short-Term Memory Network Open
Accurately forecasting wind speed on a short-term scale has become essential in the field of wind power energy. In this paper, a multi-variable long short-term memory network model (MV-LSTM) based on Pearson correlation coefficient feature…
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Filter Design for Autoregressive Moving Average Graph Filters Open
In the field of signal processing on graphs, graph filters play a crucial role in processing the spectrum of graph signals. This paper proposes two different strategies for designing autoregressive moving average (ARMA) graph filters on bo…
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Adaptive Gradient‐Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique Open
The identification problem of multivariable controlled autoregressive systems with measurement noise in the form of the moving average process is considered in this paper. The key is to filter the input–output data using the data filtering…