scholarly journals Identification of monthly municipal water demand system based on autoregressive integrated moving average model tuned by particle swarm optimization

2017 ◽  
Vol 19 (2) ◽  
pp. 261-281 ◽  
Author(s):  
Sahbi Boubaker
Keyword(s):  
Time Series ◽  
Particle Swarm Optimization ◽  
Water Demand ◽  
Moving Average ◽  
Arima Model ◽  
Particle Swarm ◽  
Demand System ◽  
Swarm Optimization ◽  
Municipal Water ◽  
Average Absolute Relative Error

In this paper, a modeling-identification approach for the monthly municipal water demand system in Hail region, Saudi Arabia, is developed. This approach is based on an auto-regressive integrated moving average (ARIMA) model tuned by the particle swarm optimization (PSO). The ARIMA (p, d, q) modeling requires estimation of the integer orders p and q of the AR and MA parts; and the real coefficients of the model. More than being simple, easy to implement and effective, the PSO-ARIMA model does not require data pre-processing (original time-series normalization for artificial neural network (ANN) or data stationarization for traditional stochastic time-series (STS)). Moreover, its performance indicators such as the mean absolute percentage error (MAPE), coefficient of determination (R2), root mean squared error (RMSE) and average absolute relative error (AARE) are compared with those of ANN and STS. The obtained results show that the PSO-ARIMA outperforms the ANN and STS approaches since it can optimize simultaneously integer and real parameters and provides better accuracy in terms of MAPE (5.2832%), R2 (0.9375), RMSE (2.2111 × 105m3) and AARE (5.2911%). The PSO-ARIMA model has been implemented using 69 records (for both training and testing). The results can help local water decision makers to better manage the current water resources and to plan extensions in response to the increasing need.

scholarly journals An ARMA Type Fuzzy Time Series Forecasting Method Based on Particle Swarm Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Erol Egrioglu ◽  
Ufuk Yolcu ◽  
Cagdas Hakan Aladag ◽  
Cem Kocak
Keyword(s):  
Time Series ◽  
Particle Swarm Optimization ◽  
Moving Average ◽  
Particle Swarm ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Swarm Optimization ◽  
Accurate Forecast ◽  
Time Invariant

In the literature, fuzzy time series forecasting models generally include fuzzy lagged variables. Thus, these fuzzy time series models have only autoregressive structure. Using such fuzzy time series models can cause modeling error and bad forecasting performance like in conventional time series analysis. To overcome these problems, a new first-order fuzzy time series which forecasting approach including both autoregressive and moving average structures is proposed in this study. Also, the proposed model is a time invariant model and based on particle swarm optimization heuristic. To show the applicability of the proposed approach, some methods were applied to five time series which were also forecasted using the proposed method. Then, the obtained results were compared to those obtained from other methods available in the literature. It was observed that the most accurate forecast was obtained when the proposed approach was employed.

A Hybrid Forecasting Model for Non-Stationary Time Series

2012 ◽  
Vol 3 (2) ◽  
pp. 67-82 ◽  
Author(s):  
Yi Xiao ◽  
Jin Xiao ◽  
Shouyang Wang
Keyword(s):  
Time Series ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Stationary Time Series ◽  
Adaptive Mutation ◽  
Forecasting Model ◽  
Feed Forward Neural Network ◽  
Swarm Optimization ◽  
Individual Fitness ◽  
Hybrid Forecasting

In time series analysis, an important problem is how to extract the information hidden in the non-stationary and noise data and combine it into a model for forecasting. In this paper, the authors propose a TEI@I based hybrid forecasting model. A novel feed forward neural network is developed based on the improved particle swarm optimization with adaptive genetic operator (IPSO-FNN) for forecasting. In the proposed IPSO, inertia weight is dynamically adjusted according to the feedback from particles’ best memories, and acceleration coefficients are controlled by a declining arccosine and an increasing arccosine function. Subsequently, a crossover rate which only depends on generation and an adaptive mutation rate based on individual fitness are designed. The parameters of FNN are optimized by binary and decimal particle swarm optimization. Further, the forecast results of IPSO-FNN are adjusted with the knowledge from text mining and an expert system. The empirical results on the container throughput forecast of Tianjin Port show that forecasts with the proposed method are much better than some other methods.

Study on Chaotic Time Series Prediction Algorithm for Kent Mapping Based on Particle Swarm Optimization

2014 ◽  
Vol 511-512 ◽  
pp. 941-944 ◽  
Author(s):  
Hong Li Bian
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Particle Swarm Optimization ◽  
Bp Neural Network ◽  
Time Series Prediction ◽  
Particle Swarm ◽  
Chaotic Time Series ◽  
Learning Ability ◽  
Swarm Optimization ◽  
Chaotic Time Series Prediction

Based on the particle swarm optimization (PSO) and BP neural network (BPNN), an algorithm for BP neural network optimized particle swarm optimization (PSOBPNN) is proposed. In the algorithm, PSO is used to obtain better network initial threshold and weight to compensate the defect of connection weight and thresholds of BPNN, thus it can make BPNN have faster convergence and greater learning ability. The efficiency of the proposed prediction method is tested by the simulation of the chaotic time series for Kent mapping. The simulations results show that the proposed method has higher forecasting accuracy compared with the BPNN, so it is proved that the algorithm is feasible and effective in the chaotic time series prediction.

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