Showing papers on "Moving-average model published in 2021"

Least-squares estimation for uncertain moving average model

Abstract: An uncertain time series (UTS) is a sequence of uncertain observed values taken sequentially in time. As a basic UTS model, an uncertain autoregressive (UAR) model has been investigated. This paper...

Analysis of the Forecast Price as a Factor of Sustainable Development of Agriculture

Abstract: Analysis of the rise in prices for consumer goods is a state’s priority task. The state assumes the obligation to regulate pricing in all spheres of consumption. First of all, the prices for essential commodities to which agricultural products belong are analyzed. The article shows the changes in prices for consumer goods of agricultural products (sugar) during a pandemic. The analysis of forecasting prices for sugar and its impact on the development of its production is carried out. The construction of the forecast model was based on extrapolation. The structure of a forecast model for price changes was based on the analysis of the time series of the Autoregressive Integrated Moving Average (ARIMA) class. This model consists of an autoregressive model and a moving average model. A forecast of the volume of domestic sugar transportation by rail has been completed. The algorithms implemented this model for searching for initial approximations and optimal parameters for the predictive model. The Hirotsugu Akaike Information Criterion (AIC) was used to select the best model. The algorithms were implemented in the Python programming language. The quality check of the description was performed with a predictive model of actual data. An economic interpretation of the rise in sugar prices and proof of the forecast’s truth obtained from a financial point of view were carried out.

Short-Term Wind Power Prediction Using Hybrid Auto Regressive Integrated Moving Average Model and Dynamic Particle Swarm Optimization

Abstract: With the upsurge in restructuring of the power markets, wind power has become one of the key factors in power generation in the smart grids and gained momentum in the recent years. The accurate wind power forecasting is highly desirable for reduction of the reserve capability, enhancement in penetration of the wind power, stability and economic operation of the power system. The time series models are extensively used for the wind power forecasting. The model estimation in the ARIMA model is usually accomplished by maximizing the log likelihood function and it requires to be re-estimated for any change in input value. This degrades the performance of the ARIMA model. In the proposed work, the model estimation of the ARIMA model is done using latest evolutionary algorithm (i.e., dynamic particle swarm optimization [DPSO]). The use of DPSO algorithm eliminates the need for re-estimation of the model coefficients for any change in input value and moreover, it improves the performance of ARIMA model. The performance of proposed DPSO-ARIMA model has been compared to the existing models.

Least squares estimation for the high-order uncertain moving average model with application to carbon dioxide emissions

Abstract: Uncertain time series analysis aims to explore how the current observation is affected by the disturbance terms and past imprecise observations characterized as uncertain variables. For the case th...

A forecasting model in managing future scenarios to achieve the sustainable development goals of thailand’s environmental law: enriching the path analysis-varima-ovi model

Abstract: The objective of this study is to develop a forecasting model for causal factors management in the future in to order to achieve sustainable development goals. This study applies a validity-based concept and the best model called “Path analysis based on vector autoregressive integrated moving average with observed variables” (Path Analysis-VARIMA-OVi Model). The main distinguishing feature of the proposed model is the highly efficient coverage capacity for different contexts and sectors. The model is developed to serve long-term forecasting (2020-2034). The results of this study show that all three latent variables (economic growth, social growth, and environmental growth) are causally related. Based on the Path Analysis-VARIMAOVi Model, the best linear unbiased estimator (BLUE) is detected when the government stipulates a new scenario policy. This model presents the findings that if the government remains at the current future energy consumption levels during 2020-2034, constant with the smallest error correction mechanism, the future CO2 emission growth rate during 2020-2034 is found to increase at the reduced rate of 8.62% (2020/2034) or equivalent to 78.12 Mt CO2 Eq. (2020/2034), which is lower than a carrying capacity not exceeding 90.5 Mt CO2 Eq. (2020-2034). This outcome differs clearly when there is no stipulation of the above scenario. Future CO2 emission during 2020-2034 will increase at a rate of 40.32% or by 100.92 Mt CO2 Eq. (2020/2034). However, when applying the Path Analysis-VARIMA-OVi Model to assess the performance, the mean absolute percentage error (MAPE) is estimated at 1.09%, and the root mean square error (RMSE) is estimated at 1.55%. In comparison with other models, namely multiple regression model (MR model), artificial neural network model (ANN model), back-propagation neural network model (BP model), fuzzy analysis network process model (FANAP model), gray model (GM model), and gray-autoregressive integrated moving average model (GM-ARIMA model), the Path Analysis-VARIMA-OVi model is found to be the most suitable tool for a policy management and planning to achieve a sustainability for Thailand.

A flexible univariate moving average time-series model for dispersed count data

Abstract: Al-Osh and Alzaid (1988) consider a Poisson moving average (PMA) model to describe the relation among integer-valued time series data; this model, however, is constrained by the underlying equi-dispersion assumption for count data (i.e., that the variance and the mean equal). This work instead introduces a flexible integer-valued moving average model for count data that contain over- or under-dispersion via the Conway-Maxwell-Poisson (CMP) distribution and related distributions. This first-order sum-of-Conway-Maxwell-Poissons moving average (SCMPMA(1)) model offers a generalizable construct that includes the PMA (among others) as a special case. We highlight the SCMPMA model properties and illustrate its flexibility via simulated data examples.

Remaining Useful Life Prediction Method for MOSFET Based on Time Series Model

Abstract: This paper presents a remaining useful life prediction method for MOSFET based on time series model. First, the degradation data of MOSFET is acquired from mathematical model. Next, correlation test is conducted to determine difference order of time-series model and Akaike information criterion (AIC) is used to determine the order of autocorrelation model and moving average model, thereby determining the parameters of time series model. Then, short-term cycle prediction is added to improve prediction accuracy and reduce accumulated error. Finally, the effectiveness of the developed life prediction model is verified using Matlab/Simulink.

Evaluating information criteria for selecting spatial processes

Abstract: Information criteria have been widely used in many quantitative applications as an effort to select the most appropriate model that describes well enough the unknown population behavior for a given dataset. Studies have shown that their performance depends on several elements and the selection of the best fitted model is not always the same for all criteria. For this purpose, this research evaluates the performance of the three most often used information criteria, such as the Akaike information criterion, the Bayesian information criterion and Hannan and Quinn information criterion, for selecting spatial processes, taking into account that the sample in spatial analysis is regarded as a realization of a spatial process that incorporates the spatial dependence between the observations. Using a Monte Carlo analysis for the three most frequently applied in practice spatial processes, such as the first-order spatial autoregressive process, SAR(1), the first-order spatial moving average process, SMA(1), and the mixed spatial autoregressive moving average process, SARMA(1, 1), this study finds that these information criteria can assist the analyst to select the true process, but their behavior depends on sample size as well as on the magnitude of the spatial parameters, leading occasionally to alternative competitive processes.

On the accuracy of ARIMA based prediction of COVID-19 spread

Abstract: COVID-19 was declared a global pandemic by the World Health Organization in March 2020, and has infected more than 4 million people worldwide with over 300,000 deaths by early May 2020. Many researchers around the world incorporated various prediction techniques such as Susceptible–Infected–Recovered model, Susceptible–Exposed–Infected–Recovered model, and Auto Regressive Integrated Moving Average model (ARIMA) to forecast the spread of this pandemic. The ARIMA technique was not heavily used in forecasting COVID-19 by researchers due to the claim that it is not suitable for use in complex and dynamic contexts. The aim of this study is to test how accurate the ARIMA best-fit model predictions were with the actual values reported after the entire time of the prediction had elapsed. We investigate and validate the accuracy of an ARIMA model over a relatively long period of time using Kuwait as a case study. We started by optimizing the parameters of our model to find a best-fit through examining auto-correlation function and partial auto correlation function charts, as well as different accuracy measures. We then used the best-fit model to forecast confirmed and recovered cases of COVID-19 throughout the different phases of Kuwait’s gradual preventive plan. The results show that despite the dynamic nature of the disease and constant revisions made by the Kuwaiti government, the actual values for most of the time period observed were well within bounds of our selected ARIMA model prediction at 95% confidence interval. Pearson’s correlation coefficient for the forecast points with the actual recorded data was found to be 0.996. This indicates that the two sets are highly correlated. The accuracy of the prediction provided by our ARIMA model is both appropriate and satisfactory.

A gamma moving average process for modelling dependence across development years in run-off triangles

Abstract: We propose a stochastic model for claims reserving that captures dependence along development years within a single triangle. This dependence is based on a gamma process with a moving average form of order which is achieved through the use of poisson latent variables. We carry out Bayesian inference on model parameters and borrow strength across several triangles, coming from different lines of businesses or companies, through the use of hierarchical priors. We carry out a simulation study as well as a real data analysis. Results show that reserve estimates, for the real data set studied, are more accurate with our gamma dependence model as compared to the benchmark over-dispersed poisson that assumes independence.

The Neural Moving Average Model for Scalable Variational Inference of State Space Models

Abstract: Variational inference has had great success in scaling approximate Bayesian inference to big data by exploiting mini-batch training. To date, however, this strategy has been most applicable to models of independent data. We propose an extension to state space models of time series data based on a novel generative model for latent temporal states: the neural moving average model. This permits a subsequence to be sampled without drawing from the entire distribution, enabling training iterations to use mini-batches of the time series at low computational cost. We illustrate our method on autoregressive, Lotka-Volterra, FitzHugh-Nagumo and stochastic volatility models, achieving accurate parameter estimation in a short time.

Forecasting COVID-19 Time Series Based on an Autoregressive Model

Abstract: When considering time-series forecasting, the application of autoregressive models is a popular and simple technique that is usually considered. In this chapter, we present the basic theoretical aspects and assumptions of the ARIMA—Autoregressive Integrated Moving Average model. It is considered for the prediction of the COVID-19 epidemiological data series of five different countries (China, United States, Brazil, Italy, and Singapore), each of them with specific curves, which are results of the virus reproduction itself but also of policies and government decisions during the pandemic spread. The discussion about the results is performed with the focus on the three evaluation criteria of the model: \(R^2\) Score, MAE, and MSE. Higher \(R^2\) Score was obtained when the sample time series was smoothly increasing or decreasing. The error metrics were higher when the prediction was performed for oscillating data series. This may indicate that the use of ARIMA models may be suitable as a prediction tool for the COVID-19 when the country is not facing severe oscillations in the number of infections.

Extended Generalized Skew Laplace Random Field: Spatial Autoregressive and Moving Average Model for Prediction of Missing Data in Skew and Heavy Tailed Data

Abstract: In this paper, we define a spatial skew and heavy-tailed random field by an extended version of multivariate generalized skew Laplace distribution. The Bayesian spatial regression model is developed to explain the spatial data. A simulation study is then carried out to validate and evaluate the performance of the proposed model. The application of this model is also demonstrated in an analysis of a geological real data set.

Theoretical properties of the sample generalized codifference function of stable moving average process

Abstract: The generalized codifference function as a dependence measure for stationary processes with infinite variance has been proposed as a generalization of the autocorrelation function. In this paper we investigate the theoretical properties of estimator of generalized codifference function of stable moving average process. Some theoretical properties of the sample codifference function of moving average process for small order are discussed.

Spectral Subsampling MCMC for Stationary Multivariate Time Series

Abstract: Spectral subsampling MCMC was recently proposed to speed up Markov chain Monte Carlo (MCMC) for long stationary univariate time series by subsampling periodogram observations in the frequency domain. This article extends the approach to stationary multivariate time series. It also proposes a multivariate generalisation of the autoregressive tempered fractionally differentiated moving average model (ARTFIMA) and establishes some of its properties. The new model is shown to provide a better fit compared to multivariate autoregressive moving average models for three real world examples. We demonstrate that spectral subsampling may provide up to two orders of magnitude faster estimation, while retaining MCMC sampling efficiency and accuracy, compared to spectral methods using the full dataset.

A New Overdispersed Integer-Valued Moving Average Model with Dependent Counting Series

Abstract: A new integer-valued moving average model is introduced. The assumption of independent counting series in the model is relaxed to allow dependence between them, leading to the overdispersion in the model. Statistical properties were established for this new integer-valued moving average model with dependent counting series. The Yule-Walker method was applied to estimate the model parameters. The estimator's performance was evaluated using simulations, and the overdispersion test of the INMA(1) process was applied to examine the dependence between counting series.

A Novel ARMA Type Possibilistic Fuzzy Forecasting Functions Based on Grey-Wolf Optimizer (ARMA-PFFs)

Abstract: This study proposes a new time series forecasting method that employs possibilistic fuzzy c-means, an autoregressive moving average model (ARMA), and a grey wolf optimizer (GWO) in type-1 fuzzy functions. Type-1 fuzzy functions (T1FFs) were used to forecast functions using an autoregressive model. However, rather than relying solely on past values of the forecast variable in a regression, the inclusion of past forecast errors improves forecasting ability. In this sense, the moving average model also employed in the proposed method. The inputs therefore are a combination of the past values of the time series and the past errors. The main idea of T1FFs is to include a new variable (or variables) that provides more information about the time series. The fuzzy c-means clustering (FCM) algorithm was used to quantify the values of this new variable. The degrees of memberships were obtained for each observation in each cluster and these membership grades were used as a new variable in the input matrix. Studies in the literature, however, have shown certain restrictions for FCM, such as sensitive noise and coincidence cluster centers. Consequently, possibilistic FCM is employed in T1FFs to overcome the aforementioned limitations. Because of the non-derivative objective function of ARMA type possibilistic fuzzy forecasting functions, the GWO was adapted in order to obtain coefficients for the model. The performance of the proposed ARMA type-1 fuzzy possibilistic functions was validated using 16 practical time-series.

Accurately predicting heat transfer performance of ground-coupled heat pump system using improved autoregressive model.

Abstract: Nowadays, ground-coupled heat pump system (GCHP) becomes one of the most energy-efficient systems in heating, cooling and hot water supply. However, it remains challenging to accurately predict thermal energy conversion, and the numerical calculation methods are too complicated. First, according to seasonality, this paper analyzes four variables, including the power consumption of heat pump, the power consumption of system, the ratios of the heating capacity (or the refrigerating capacity) of heat pump to the operating powers of heat pump and to the total system, respectively. Then, heat transfer performance of GCHP by historical data and working parameters is predicted by using random forests algorithm based on autoregressive model and introducing working parameters. Finally, we conduct experiments on 360-months (30-years) data generated by GCHP software. Among them, the first 300 months of data are used for training the model, and the last 60 months of data are used for prediction. Benefitting from the working condition inputs it contained, our model achieves lower Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) than Exponential Smoothing (ES), Autoregressive Model (AR), Autoregressive Moving Average Model (ARMA) and Auto-regressive Integrated Moving Average Model (ARIMA) without working condition inputs.

Research on Gas Concentration Prediction Based on Wavelet Denoising and ARIMA Model

Abstract: In order to improve the reliability and accuracy of mine gas concentration prediction, a prediction model based on wavelet noise reduction and autoregressive differential moving average model (ARIMA) is proposed. the original data is decomposed, thresholded and reconstructed, and the noise in the time series data is stripped, and then the ARIMA module of Python is called to build a prediction model to fit the prediction data, The ARIMA (2,1,1) model parameters were selected to fit the best prediction model, and the prediction effect was tested. Research shows that the method based on wavelet noise reduction and ARIMA prediction model can effectively improve the prediction accuracy and reliability of gas concentration prediction in the short-term. The prediction results of this algorithm are compared with other prediction models. The prediction model can not only reflect the change trend of gas emission concentration, but also has high fitting effect and prediction accuracy.

An irregularly spaced first-order moving average model

Abstract: A novel first-order moving-average model for analyzing time series observed at irregularly spaced intervals is introduced. Two definitions are presented, which are equivalent under Gaussianity. The first one relies on normally distributed data and the specification of second-order moments. The second definition provided is more flexible in the sense that it allows for considering other distributional assumptions. The statistical properties are investigated along with the one-step linear predictors and their mean squared errors. It is established that the process is strictly stationary under normality and weakly stationary in the general case. Maximum likelihood and bootstrap estimation procedures are discussed and the finite-sample behavior of these estimates is assessed through Monte Carlo experiments. In these simulations, both methods perform well in terms of estimation bias and standard errors, even with relatively small sample sizes. Moreover, we show that for non-Gaussian data, for t-Student and Generalized errors distributions, the parameters of the model can be estimated precisely by maximum likelihood. The proposed IMA model is compared to the continuous autoregressive moving average (CARMA) models, exhibiting good performance. Finally, the practical application and usefulness of the proposed model are illustrated with two real-life data examples.