Modelling and Forecasting Climate Time Series with State-Space Model

Abstract

This study modelled and estimated climatic data using the state-space model. The study was specifically to identify the pattern of the trend movement i.e., increase or decrease in the occurrence of the climatic change; to use of Univariate Kalman Filter for the computation of the likelihood function for climatic projections; to modelling the climatic dataset using the state-space model and to assess the forecasting power of the state-space models. The data used for the work includes temperature and rainfall for periods January 1991 to December 2017. The data are tested for normality. Shapiro-Wilk, Anderson-Darling and Kolmogorov-Smirnov test of normality for the climatic data all showed that the variables are not normally distributed. The work spans the use of breaking trend regression model to fit climatic data to estimate the slopes which show much increase in climatic data has been recorded from the initial time data collection until the present. Investigations and diagnostic are carried out by checking for corrections in the residuals and also checking for periodicity in the residuals. The results of this investigation show significant autocorrelation in the residuals indicating the presence of underlying noise terms which is not accounted for. By treating the residual as an autoregressive moving average (ARMA) process whereby we can obtain its spectral density, the result from the parametric spectral estimate shows underlying periodic patterns for monthly data, thus, leads to a discussion on the need to treat climatic data as a structural time series model. We select appropriate models by considering the goodness of fit of the model by comparing the Akaike information criterion (AIC) values. Parameters are estimated and accomplished with some measures of precision.

Modeling and Forecasting Selected Meteorological Parameters for the Environmental Awareness in Sub-Sahel West Africa Stations

“…The forecasting of future weather sequence variables for environmental awareness is based on the spell of historical categorization, which is known as an essential metric for atmospheric parameter forecasting and modeling [1][2][3][4][5][6][7][8][9]. Furthermore, agricultural products are subject to meteorological, ecological, and environmental variation [8,[10][11][12][13][14], wherein planetary and chronological qualities have a very important link over the environment by applying time series modeling i.e ARIMA model [5,[15][16][17][18]. International heating impacts, according to [19][20][21][22], contribute to the development of the atmospheric structure; nevertheless, this effect is on the upcoming alterations in the cli-matic growth of the environment, where the occurrence and size of diverse happenings affect the growth of any region [6,23].…”

Improving forecasting accuracy using quantile regression neural network combined with unrestricted mixed data sampling

“…The authors stated that GARCH-MIDAS models could significantly beat their single-regime counterparts when forecasting out-of-sample oil volatility. Building forecasting models that can provide a better prediction is encouraged by stakeholders to optimize operations and gain competitive advantages [38][39][40]. The unemployment rate is the best indicator to forecast quarterly GDP growth, according to a study by Kingnetr et al [33].…”

Forecasting of the epidemiological situation: Case of COVID-19 in Morocco

“…According to Dost Khan [15] and T. Latunde [16], they represent the popular autoregressive integrated moving average (ARIMA) will be used to forecast the cumulative number of confirmed, recovered cases, and the number of deaths from COVID-19 for the specific country.…”