Forecasting performance of nonlinear time-series models: an application to weather variable

Karimuzzaman,

doi:10.1007/s40808-020-00826-6

Cited by 14 publications

(6 citation statements)

References 51 publications

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“…Various non-linear and non-stationary time series forecasting methods presented in the literature are considered and classified based on how they are applied to predict time series data for real-world problems [1,2], [3,8], [9,10], [11,12]. Prediction methods can be classified based on prerequisites or approaches to overcome non-stationarity and nonlinearity, as they assume the following features: a known trend shape, piecewise stationarity of signals, progressively varying parameters, or decomposability of a signal into stationary segments in the transformed domain, and they are either parametric or non-parametric, depending on whether the predictor takes a certain form or is built solely in accordance with the data (for example, the number of latent variables may vary).…”

Section: Analysis Of Literary Datamentioning

confidence: 99%

“…However, the difference tends to increase high-frequency noise in the time series, and more effort is required to determine the order of the ARMA model. To incorporate nonlinearity into the ARMA framework, advanced models such as thresholding AR (TAR) models [9,10], self-excited AR (SETAR) models [11], and smooth transition AR (STAR) models [12] are used. They were developed for non-linear forecasting.…”

Section: Analysis Of Literary Datamentioning

confidence: 99%

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Modeling and forecasting of nonlinear nonstationary processes based on the Bayesian structural time series

Kalinina¹,

Gozhyj²

2022

AAIT

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The article describes an approach to modelling and forecasting non-linear non-stationary time series for various purposes using Bayesian structural time series. The concepts of non-linearity and non-stationarity, as well as methods for processing non-linearity’sand non-stationarity in the construction of forecasting models are considered. The features of the Bayesian approach in the processing of nonlinearities and nonstationaryare presented. An approach to the construction of probabilistic-statistical models based on Bayesian structural models of time series has been studied. Parametric and non-parametric methods for forecasting non-linear and non-stationary time series are considered. Parametric methods include methods: classical autoregressive models, neural networks, models of support vector machines, hidden Markov models. Non-parametric methods include methods: state-space models, functional decomposition models, Bayesian non-parametric models. One of the types of non-parametric models isBayesian structural time series. The main features of constructing structural time series are considered. Models of structural time series are presented. The process of learning the Bayesianstructural model of time series is described. Training is performed in four stages: setting the structure of the model and a priori probabilities; applying a Kalman filter to update state estimates based on observed data;application of the “spike-and-slab”method to select variables in a structural model; Bayesian averaging to combine the results to make a prediction. An algorithm for constructing a Bayesian structural time seriesmodel is presented. Various components of the BSTS model are considered andanalysed, with the help of which the structures of alternative predictive models are formed. As an example of the application of Bayesian structural time series, the problem of predicting Amazon stock prices is considered. The base dataset is amzn_share. After loading, the structure and data types were analysed, and missing values were processed. The data are characterized by irregular registration of observations, which leads to a large number of missing values and “masking” possible seasonal fluctuations. This makes the task of forecasting rather difficult. To restore gaps in the amzn_sharetime series, the linear interpolation method was used. Using a set of statistical tests (ADF, KPSS, PP), the series was tested for stationarity. The data set is divided into two parts: training and testing. The fitting of structural models of time series was performed using the Kalman filterand the Monte Carlo method according to the Markov chain scheme. To estimate and simultaneously regularize the regression coefficients, the spike-and-slab method was applied. The quality of predictive models was assessed.

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Section: Analysis Of Literary Datamentioning

confidence: 99%

Section: Analysis Of Literary Datamentioning

confidence: 99%

Modeling and forecasting of nonlinear nonstationary processes based on the Bayesian structural time series

Kalinina¹,

Gozhyj²

2022

AAIT

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“…An areal indoor temperature [4,5] forecasting task by applying a study on the techniques of deep learning, studying performance is owed to different hyper-parameter configuration. Comparison based on the accuracy of forecasting in various methods like feed-forward neural network (ANNs), linear autoregressive (linear AR), logistic smooth transition autoregressive model (LSTAR), and self-exciting threshold auto-regression (SETAR) is proposed in [6], and close-fitting with the hyper-parameters and determination of regime. An important issue in a competitive market like the electric market is efficient modeling and prediction of electricity prices and demand.…”

Section: Related Workmentioning

confidence: 99%

“…( 5) while considering the Gaussian function as basis function corresponding definition can be given by Eq. (6).…”

Section: Adaptive Kernel Based Rbfmentioning

confidence: 99%

Forecasting of Trend-Cycle Time Series Using Hybrid Model Linear Regression

Ashwini¹,

Nagaveni²,

Singh³

2022

Intelligent Automation &Amp; Soft Computing

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“…Hsiao and Hansen (2011) modelled city-pair air passenger demand at the route level using a type of discrete choice method which are widely used for the analysis of individual choice behaviour. In addition, linear multivariate time series can be modelled by vector autoregression (VAR) (Cao and Tay, 2003) and nonlinear time-series models nonlinear VAR (Samadi et al, 2019) and more (Karimuzzaman and Hossain, 2020;Comi et al, 2020).…”

Section: Background On Air Passenger Demand Forecastingmentioning

confidence: 99%

The chaos on U.S. domestic airline passenger demand forecasting caused by COVID-19

Jafari¹

2022

IJBFMI

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Commercial aviation is a major contributor to the US economy, directly or indirectly generating approximately US$680 billion, or 4% of GDP, and supporting millions of jobs. Approximately 965 million passengers flew to US destinations in 2017 (https://rosap.ntl.bts.gov/view/dot/37861). Given the importance of the industry, accurate forecasting of air passenger demand is valuable, and the most sophisticated forecasting technologies can be applied to this endeavour. The ongoing COVID-19 crisis has had an unprecedented impact on air traffic. Effective forecast of passenger demand would benefit airlines to develop adequate recovery plans and prevent (or minimise) any catastrophe in handling passengers during and post pandemic. The purpose of this study is to investigate COVID-19's impact on the US domestic air passengers demand, identify the most influential features on air passenger demand, and design more accurate forecast models. In addition, we address a computational challenge in developing forecasting models due to the volatility of the recent data as a result of the COVID-19 crisis. We use both traditional and artificial intelligence methods and discuss their capabilities to handle the challenge.

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Forecasting performance of nonlinear time-series models: an application to weather variable

Cited by 14 publications

References 51 publications

Modeling and forecasting of nonlinear nonstationary processes based on the Bayesian structural time series

Modeling and forecasting of nonlinear nonstationary processes based on the Bayesian structural time series

Forecasting of Trend-Cycle Time Series Using Hybrid Model Linear Regression

The chaos on U.S. domestic airline passenger demand forecasting caused by COVID-19

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