Sequence in Hybridization of Statistical and Intelligent Models in Time Series Forecasting

Hajirahimi, Zahra; Khashei, Mehdi

doi:10.1007/s11063-020-10294-9

Cited by 9 publications

(3 citation statements)

References 25 publications

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“…Hajirahimi and Khashei [21] used ARIMA, Multilayer Perceptron (MLP), and SVM models to make a hybrid model for time-series forecasting. Five datasets are used for prediction, i.e., British pound/US dollar exchange rate, Wolf's Sunspot, Nikkei 225 stock price, the Colorado wind speed, and Canadian Lynx.…”

Section: Hybrid Modelsmentioning

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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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: Hybrid Modelsmentioning

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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“…In 2020, Hajirahimi and Khashei [46] demonstrated that using a non-linear intelligent model as the first component in the sequential modeling system leads to more accurate results when compared to models whose first step is to use a linear technique. Thus, they proposed a Nonlinear-Linear approach with the SVM and MLP techniques.…”

Section: Related Workmentioning

confidence: 99%

A Nonlinear Optimized Hybrid System For Energy Consumption Forecasting From Smart Meters

Alves¹,

Oliveira²,

Madeiro³

et al. 2022

Learn. Nonlin. Mod.

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Smart grids are an alternative to minimize environmental impacts, such as CO2 emissions, and improve the efficiency of electricity consumption in buildings. Power grids enable adequate management and monitoring of consumption because of the periodic storage of measurements and easy access to them. In this scenario, an accurate prediction is a challenging task. Forecasting of consumption series is a defiant problem because data present linear and nonlinear patterns, and a dependence on external variables may be observed. Hybrid models are an alternative to mapping both patterns, which have been widely used to forecast load time series. Autoregressive Integrated Moving Average (ARIMA) and Support Vector Regression (SVR) models are used for this purpose, to map the linear and nonlinear patterns of the series, respectively. In this paper, a nonlinear optimized hybrid system based on ARIMA, SVR, and Particle Swarm Optimization (PSO) is proposed. The system can be divided into three steps. First, the linear patterns are predicted by the statistical model ARIMA. Then, the residual series is modeled using an optimized SVR, in which the parameters are selected from the PSO. One particularity from the proposal is to incorporate the choice of the topology and the inertia coefficient into the system. Lastly, the predictions are combined using the SVR. The simulations were conducted using a real database from smart meters of a building in Taiwan. To evaluate the performance of the proposed method, four related approaches were implemented and compared: a single ARIMA, two linear combination systems, and one non-linear combination system. The results show a superiority of the proposed method in terms of the metrics Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE).

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“…Thus, the injection of disinformation is bidirectional, especially after accessing a large amount of distrib-uted energy sources. [19][20][21] Literature [22] proposes an false data injection attack (FDIA) detection method based on secure federation deep learning, inspired by federation learning, combining Transformer, federation learning, and Paillier cryptosystem.…”

Section: Introductionmentioning

confidence: 99%

Data‐Driven Detection of Integrated Energy Theft Based on VMD‐MIC and Informer

Liu,

Wang,

Liu

et al. 2024

Advcd Theory and Sims

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Integrated energy systems (IES) may experience energy theft through disinformation injection attacks. With electricity as the primary target, the attacks are essentially two way. They gain additional benefits by manipulating meter readings on both the consumer and supply side. Regression based deep learning models with feature engineering can be used to detect these attacks. To detect energy theft more efficiently, this paper proposes an integrated energy systems theft detection (IESTD) based on variational modal decomposition‐maximal information coefficient (VMD‐MIC) and Informer. And using kernel density estimation (KDE), the error between the predicted and true values is fitted as a probability density function (PDF).The thresholds for the confidence intervals of the PDF and the thresholds for the number of outliers are set as the detection metrics. Simulations are performed on the three generated datasets. The results show that the model has good detection accuracy and strong robustness.

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Sequence in Hybridization of Statistical and Intelligent Models in Time Series Forecasting

Cited by 9 publications

References 25 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

A Nonlinear Optimized Hybrid System For Energy Consumption Forecasting From Smart Meters

Data‐Driven Detection of Integrated Energy Theft Based on VMD‐MIC and Informer

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