In this work we propose a Bayesian approach for the parameter estimation problem of stochastic autoregressive models of order p, AR(p), applied to the streamflow forecasting problem. Procedures for model selection, forecasting and robustness evaluation through Monte Carlo Markov Chain (MCMC) simulation techniques are also presented. The proposed approach is compared with the classical one by Bo...

This study compares the forecasting performance of various Autoregressive integrated moving average (ARIMA) models by using time series data. Primarily, The Box-Jenkins approach is considered here for forecasting. For empirical analysis, we used CPI as a proxy for inflation and employed quarterly data from 1970 to 2006 for Pakistan. The study classified two important models for forecasting out ...

In this paper, two CI techniques, namely, single multiplicative neuron (SMN) model and adaptive neurofuzzy inference system (ANFIS), have been proposed for time series prediction. A variation of particle swarm optimization (PSO) with co-operative sub-swarms, called COPSO, has been used for estimation of SMN model parameters leading to COPSO-SMN. The prediction effectiveness of COPSO-SMN and ANF...

In this paper, forecasting of monthly electricity load using Box-Jenkins methodology and feed forward neural networks is discussed. This study investigates application of neural networks models and the results of neural networks will be compared with those obtained by Box-Jenkins method.

In recent decades, Fuzzy Time Series (FTS) has become a competitive, sometimes complementary, approach to classical time series methods such as that of Box-Jenkins. This study two different purposes: theoretical purpose, presenting an overview the fuzzy logic and models, practical which is estimate forecast monthly international coffee prices during period 2000-2022. Analysing forecasting dynam...

drought is a gradual phenomenon and cause important changes in water resources, agriculture and so on. due to the stochastic behavior of factors that influence the occurrence and intense of drought, one can consider this phenomenon as a stochastic process. with respect to the importance of predicted drought severity and its vital influence in designing and operating water resources systems, thi...

High-order ARX models can be used to approximate a quite general class of linear systems in a parametric model structure, and wellestablished methods can then be used to retrieve the true plant and noise models from the ARX polynomials. However, this commonly used approach is only valid when the plant is stable or if the unstable poles are shared with the true noise model. In this contribution,...

In recent years Singular Spectrum Analysis (SSA), used as a powerful technique in time series analysis, has been developed and applied to many practical problems. this paper, the performance of SSA tech nique considered by applying it well-known data set, namely, monthly accidental deaths USA. The results are com pared with those obtained using Box-Jenkins SARIMA models, ARAR algorithm Holt-Win...

To assess the groundwater potential, analyses of trend decomposition, time series and frequency between various types of data in hydrologic setting and between different basins is required for local and regional studies. In this paper, ground water level fluctuations and Nitrate concentrations of kabudarahang aquifer were investigated with application of time series models. Simulated values wer...