Many of the existing autoregressive moving average (ARMA) forecast models are based on one main factor. In this paper, we proposed a new two-factor first-order ARMA forecast model based on fuzzy fluctuation logical relationships of both a main factor...
The rates of continuous evolution plays a crucial role in understanding the pace at which species evolve. Various statistical models have been developed to estimate the rates of continuous trait evolution for a group of related species evolving along...
Stock market performance is one key indicator of the economic condition of a country, and stock price forecasting is important for investments and financial risk management. However, the inherent nonlinearity and complexity in stock price movements i...
With rapid urban development, the “waste-free city” concept has emerged. Therefore, the accurate prediction of the amount of C&D waste is of great importance. However, many countries and regions, including China, have not yet establis...
Water resource is considered as a significant factor in the development of regional environment and society. Water consumption prediction can provide an important decision basis for the regional water supply scheduling optimizations. According to the...
Background: Pulse rate variability (PRV), a critical biomarker of autonomic nervous system (ANS) function, is typically evaluated using the pulse-to-pulse interval (PPI) signal extracted from a photoplethysmogram (PPG). Although PPGs have been widely...
Aiming to address the complexity and challenges of predicting pure electric vehicle (EV) sales, this paper integrates a time series model, support vector machine and combined model to forecast EV sales in China. Firstly, a seasonal autoregressive int...
The present work proposes a new class of model for random variables with support in the positive real line, this model explains the conditional quantile and is an alternative for modeling data that indicate asymmetric behavior and heavy tails. We pre...
This research focuses on the study of stochastic processes with irregularly spaced time intervals, which is present in a wide range of fields such as climatology, astronomy, medicine, and economics. Some studies have proposed irregular autoregressive...
Autoregressive moving average (ARMA) models are important in many fields and applications, although they are most widely applied in time series analysis. Expanding the ARMA models to the case of various complex data is arguably one of the more challe...
The Gegenbauer autoregressive moving-average (GARMA) model is pivotal for addressing non-additivity, non-normality, and heteroscedasticity in real-world time-series data. While primarily recognized for its efficacy in various domains, including the h...
The beta autoregressive moving average (
The accurate estimation and prediction of lithium-ion battery state of health are one of the important core technologies of the battery management system, and are also the key to extending battery life. However, it is difficult to track state of heal...
In this work, we introduce an extension of the so-called beta autoregressive moving average (
In this paper, we introduce a new dynamic model for time series based on the Chen distribution, which is useful for modeling asymmetric, positive, continuous, and time-dependent data. The proposed Chen autoregressive moving average (CHARMA) model com...
This study first analyzes the national and global infection status of the Coronavirus Disease that emerged in 2019 (COVID-19). It then uses the trend comparison method to predict the inflection point and Key Point of the COVID-19 virus by comparison...
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 moving average...
In this paper, we compare the effects of forecasting demand using individual (disaggregated) components versus first aggregating the components either fully or into several clusters. Demand streams are assumed to follow autoregressive moving average...
Based on the panel data of atmospheric environmental pollution in Hunan Province from 2016 to 2023, the autoregressive integrated moving average model (ARIMA) is introduced to evaluate and predict the current status of atmospheric environmental quali...
Ningbo Zhoushan Port and Shanghai Port, as the top two ports in China in terms of port cargo throughput, play a crucial role in facilitating international trade and shipping. The accurate forecasting of the cargo throughput at these ports is essentia...
Many high formwork systems are currently equipped with health monitoring systems, and the analysis of the data obtained can determine whether high formwork is a hazard. Therefore, the post-processing of monitoring data has become an issue of widespre...
E-commerce is becoming more and more the main instrument for selling goods to the mass market. This led to a growing interest in algorithms and techniques able to predict products future prices, since they allow us to define smart systems able to imp...
This study introduces a hybrid AutoRegressive Integrated Moving Average (ARIMA)—Long Short-Term Memory (LSTM) model for predicting and managing sugarcane pests and diseases, leveraging big data for enhanced accuracy. The ARIMA component efficie...
To improve the accuracy and stability of new energy vehicle (NEV) sales forecasting in China, this study develops a hybrid forecasting framework integrating the Informer model, autoregressive integrated moving average (ARIMA), and the Boomerang Aerod...
The automotive industry faces continuing challenges with regard to advancing sustainability and reducing energy consumption and vehicle emissions. South Africa accounts for half of the total CO2 emissions in Africa and is the world’s 12th-large...
Compensation of data loss in the state estimation plays an indispensable role in efficient and stable control and communication systems. However, accurate compensation of data loss in the state estimation is extremely challenging issue. To cater this...
In this paper, the spatio-temporal (multi-channel) linear models, which use temporal and the neighbouring wind speed measurements around the target location, for the best short-term wind speed forecasting are investigated. Multi-channel autoregressiv...
Short-term building energy consumption prediction is of great significance for the optimized operation of building energy management systems and energy conservation. Due to the high-dimensional nonlinear characteristics of building heat loads, tradit...
South Africa’s coal consumption accounts for 69.6% of the total energy consumption of South Africa, and this represents more than 88% of African coal consumption, taking the first place in Africa. Thus, predicting the coal demand is necessary,...
Time series analysis of daily stock data and building predictive models are complicated. This paper presents a comparative study for stock price prediction using three different methods, namely autoregressive integrated moving average, artificial neu...
Transfer Entropy has been applied to experimental datasets to unveil causality between variables. In particular, its application to non-stationary systems has posed a great challenge due to restrictions on the sample size. Here, we have investigated...
Accurate forecasting of China’s Consumer Price Index (CPI) is crucial for effective macroeconomic policymaking, yet remains challenging due to structural breaks and nonlinear dynamics inherent in the inflation process. Traditional linear models...
The outbreak of the COVID-19 has brought about huge economic loss and civil aviation industries all over the world have suffered severe damage. An effective method is urgently needed to accurately predict air-transport demand under the influences of...
The number of wind-generating resources has increased considerably, owing to concerns over the environmental impact of fossil-fuel combustion. Therefore, wind power forecasting is becoming an important issue for large-scale wind power grid integratio...
Nonlinear autoregressive exogenous (NARX), autoregressive integrated moving average (ARIMA) and multi-layer perceptron (MLP) networks have been widely used to predict the appearance value of future points for time series data. However, in recent year...
Water use patterns were explored for three small communities that are located in proximity to agricultural fields and rely on their local wells for potable water supply. High-resolution water use data, collected over a four-year period, revealed sign...
To improve the overall accuracy of tidal forecasting and ameliorate the low accuracy of single harmonic analysis, this paper proposes a combined tidal forecasting model based on harmonic analysis and autoregressive integrated moving average–sup...
When the mechanical transmission mechanism fails, such as gears and bearings in the gearbox, its vibration signal often appears as a periodic impact. Considering the influence of noise, however, the fault signal is often submerged in the noise, so it...
This study uses two different time series forecasting approaches (parametric and non-parametric) to assess a frequency and magnitude forecasting of earthquakes above Mw 4.0 in Northeastern Algeria. The Autoregressive Integrated Moving Average (ARIMA)...
In engineering and technical fields, a large number of sensors are applied to monitor a complex system. A special class of signals are often captured by those sensors. Although they often have indirect or indistinct relationships among them, they sim...
In view of the large output noise and low precision of the Micro-electro-mechanical Systems (MEMS) gyroscope, the virtual gyroscope technology was used to fuse the data of the MEMS gyroscope to improve its output precision. Random error model in the...
This paper presents a new, accurate load forecasting technique robust to fluctuations due to unusual load behavioral changes in buildings, i.e., the potential for small commercial buildings with heterogeneous stores. The proposed scheme is featured w...
Recent developments in machine learning and deep learning have led to the use of multiple algorithms to make better predictions. Surgical units in hospitals allocate their resources for day surgeries based on the number of elective patients, which is...
Accurate and reliable air quality predictions are critical to the ecological environment and public health. For the traditional model fails to make full use of the high and low frequency information obtained after wavelet decomposition, which easily...
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 forecasting...
Suvilahti, a suburb of the city of Vaasa in western Finland, was the first area to use seabed sediment heat as the main source of heating for a high number of houses. Moreover, in the same area, a unique land uplift effect is ongoing. The aim of this...
Since the prime days of stochastic hydrology back in 1960s, autoregressive (AR) and moving average (MA) models (as well as their extensions) have been widely used to simulate hydrometeorological processes. Initially, AR(1) or Markovian models with Ga...
In order to ensure the driving safety of vehicles in windy environments, a wind monitoring and warning system is widely used, in which a wind speed prediction algorithm with better stability and sufficient accuracy is one of the key factors to ensure...
The municipal solid waste incineration (MSWI) process plays a vital role in promoting ecological civilization and sustainable development. Accurate multi-step CO2 prediction in MSWI is particularly difficult due to complex combustion dynamics and hig...
Forecasting stock prices remains a central challenge in financial modelling, as markets are influenced by market sentiment, firm-level fundamentals and complex interactions between macroeconomic and microeconomic factors, for example. This study eval...
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