Autoregressive–moving-average model ≈ Autoregressive–moving-average model
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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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Short-Term Electric Load Forecasting Using Echo State Networks and PCA Decomposition Open
In this paper we approach the problem of forecasting a time-series of electrical load measured on the ACEA power grid, the company managing the electricity distribution in the city of Rome – Italy, with an Echo State Network considering tw…
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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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Distributed Autoregressive Moving Average Graph Filters Open
We introduce the concept of autoregressive moving average (ARMA) filters on a graph and show how they can be implemented in a distributed fashion. Our graph filter design philosophy is independent of the particular graph, meaning that the …
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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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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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ARMAX-Based Transfer Function Model Identification Using Wide-Area Measurement for Adaptive and Coordinated Damping Control Open
One of the main drawbacks of the existing oscillation damping controllers that are designed based on offline dynamic models is adaptivity to the power system operating condition. With the increasing availability of wide-area measurements a…
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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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Short-term wind power forecasting using hybrid method based on enhanced boosting algorithm Open
Day-ahead wind power forecasting plays an essential role in the safe and economic use of wind energy, the comprehending- intrinsic complexity of the behavior of wind is considered as the main challenge faced in improving forecasting accura…
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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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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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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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Predicting the Water Level Fluctuation in an Alpine Lake Using Physically Based, Artificial Neural Network, and Time Series Forecasting Models Open
Accurate prediction of water level fluctuation is important in lake management due to its significant impacts in various aspects. This study utilizes four model approaches to predict water levels in the Yuan-Yang Lake (YYL) in Taiwan: a th…
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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…
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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 …