A Novel Parallel Hybrid Model Based on Series Hybrid Models of ARIMA and ANN Models

Hajirahimi, Zahra; Khashei, Mehdi

doi:10.1007/s11063-021-10732-2

Cited by 12 publications

(6 citation statements)

References 30 publications

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“…Te ARIMA model is based on the idea that values of the past will be used to forecast the future in time-series data. Te ARIMA model works on three principles: AutoRegression (AR), Integration (I), and Moving Average (MA) [36].…”

Section: Arima Modelmentioning

confidence: 99%

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Aquila Optimizer-Based Hybrid Predictive Model for Traffic Congestion in an IoT-Enabled Smart City

Chahal,

Gulia,

Gill

et al. 2024

International Journal of Intelligent Systems

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Effective traffic congestion prediction is need of the hour in a modern smart city to save time and improve the quality of life for citizens. In this study, AB_AO (ARIMA Bi-LSTM using Aquila optimizer), a hybrid predictive model, is proposed using the most effective time-series data prediction statistical model ARIMA (Autoregressive Integrated Moving Average) and sequential predictive Deep Learning (DL) technique LSTM (Long Short-Term Memory) which helps in traffic congestion prediction with a minimum error rate. Also, the Aquila optimizer (AO) is used to elevate the adequacy of the AB_AO model. Three road traffic datasets of different cities from the “CityPulse EU FP7 project” are used to implement the proposed hybrid model. In a time-series dataset, two components need to be handled with care, i.e., linear and nonlinear. In this study, the ARIMA model has been used to manage linear components and Bi-LSTM is used to handle nonlinear components of the time-series dataset. The Aquila Optimizer (AO) is used for hyperparametric tuning to enhance the performance of Bi-LSTM. Error measurement parameters like the Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE) are used to validate the results. A detailed mathematical and empirical analysis is given to justify the performance of the AB_AO model using an ablation study and comparative analysis. The AB_AO model acquires more stable and precise results with MSE as 18.78, MAE as 3.18, and MAPE as 0.21 than other models. It may further help to predict the vehicle count on the road, which may be of great help in reducing wastage of time in traffic congestion.

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Section: Arima Modelmentioning

confidence: 99%

“…After experimental results, it is found that EEMD + ANN gave MAE as 3.93 and MAPE as 6.72. Te study [36] Lilhore et al [46] gave a framework for an intelligent transport management system. Te authors presented Adaptive Trafc-Management system (ATM) to monitor vehicles, infrastructure, and events in a smart city.…”

Section: Comparative Analysis Tablementioning

confidence: 99%

Aquila Optimizer-Based Hybrid Predictive Model for Traffic Congestion in an IoT-Enabled Smart City

Chahal,

Gulia,

Gill

et al. 2024

International Journal of Intelligent Systems

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“…We use data on Pakistan's gold prices from 1/7/2003 to 1/6/2021 for empirical study. The objective [29] is to develop a comprehensive hybrid model that integrates ARIMA, MLP, ARIMA-MLP, and MLP-ARIMA to analyse all conceivable combinations of linear and nonlinear patterns. To find mixed linear/nonlinear patterns in time series, two models were developed: ARIMA-MLP and MLP-ARIMA.…”

Section: Related Workmentioning

confidence: 99%

Time series-based workload prediction using the statistical hybrid model for the cloud environment

Devi

Valli²

2022

Computing

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Resource management is addressed using infrastructure as a service. On demand, the resource management module effectively manages available resources. Resource management in cloud resource provisioning is aided by the prediction of central processing unit (CPU) and memory utilization. Using a hybrid ARIMA-ANN model, this study forecasts future CPU and memory utilization. The range of values discovered is utilized to make predictions, which is useful for resource management. In the cloud traces, the ARIMA model detects linear components in the CPU and memory utilization patterns. For recognizing and magnifying nonlinear components in the traces, the artificial neural network (ANN) leverages the residuals derived from the ARIMA model. The resource utilization patterns are predicted using a combination of linear and nonlinear components. From the predicted and previous history values, the Savitzky-Golay filter finds a range of forecast values. Point value forecasting may not be the best method for predicting multi-step resource utilization in a cloud setting. The forecasting error can be decreased by introducing a range of values, and we employ as reported by Engelbrecht HA and van Greunen M (in: Network and Service Management (CNSM), 2015 11th International Conference, 2015) OER (over estimation rate) and UER (under estimation rate) to cope with the error produced by over or under estimation of CPU and memory utilization. The prediction accuracy is tested using statistical-based analysis using Google's 29-day trail and BitBrain (BB).

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“…Based on the stock prices of 10 companies, it was found that the hybrid methodology performed better than the ARIMA model or the SVM model in predicting stock prices. Hajirahimi and Khashei proposed a new parallel hybrid model to implement an integrated hybrid framework in which all pure linear and nonlinear patterns in real time series can be appropriately simulated [19]. Li et al [20] built some combined prediction models including ARIMA + SVM and ARIMA + ANN based on the artificial intelligence technique.…”

Section: Introductionmentioning

confidence: 99%

Combining Autoregressive Integrated Moving Average Model and Gaussian Process Regression to Improve Stock Price Forecast

Tu,

Huang,

et al. 2024

Mathematics

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Stock market performance is one key indicator of the economic condition of a country, and stock price forecasting is important for investments and financial risk management. However, the inherent nonlinearity and complexity in stock price movements imply that simple conventional modeling techniques are not adequate for stock price forecasting. In this paper, we present a hybrid model (ARIMA + GPRC) which combines the autoregressive integrated moving average (ARIMA) model and Gaussian process regression (GPR) with a combined covariance function (GPRC). The proposed hybrid model can account for both the linearity and nonlinearity in stock price movements. Based on daily data on three stocks listed on the Shanghai Stock Exchange (SSE), it is found that GPRC outperforms GPR with a single covariance function. Further, the proposed hybrid model is compared with the ARIMA model, artificial neural network (ANN), and GPRC model. Based on the forecasting trend and the statistical performance of the four models, the ARIMA + GPRC model is found to be the dominant model for stock price forecasting and can significantly improve forecasting performance.

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A Novel Parallel Hybrid Model Based on Series Hybrid Models of ARIMA and ANN Models

Cited by 12 publications

References 30 publications

Aquila Optimizer-Based Hybrid Predictive Model for Traffic Congestion in an IoT-Enabled Smart City

Aquila Optimizer-Based Hybrid Predictive Model for Traffic Congestion in an IoT-Enabled Smart City

Time series-based workload prediction using the statistical hybrid model for the cloud environment

Combining Autoregressive Integrated Moving Average Model and Gaussian Process Regression to Improve Stock Price Forecast

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