A Hybrid Model Based on ANFIS and Empirical Mode Decomposition for Stock Forecasting

Wei, Liang-Ying

doi:10.7763/joebm.2015.v3.209

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…Furthermore, EVs are abundant in urban areas, and EV users' travel behavior is influenced by many random factors, resulting in increasingly complicated fluctuations in the charging load of EVs. Given this problem, accurate forecasting by using a short-term load forecasting model on a single time scale is difficult [29]. Short-term load forecasting can be enhanced by decomposing the load into multiple intrinsic mode functions and then separately predicting and reconstructing the sub-model prediction results [30].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN

Zhang

Liu

2022

Energies

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The large fluctuations in charging loads of electric vehicles (EVs) make short-term forecasting challenging. In order to improve the short-term load forecasting performance of EV charging load, a corresponding model-based multi-channel convolutional neural network and temporal convolutional network (MCCNN-TCN) are proposed. The multi-channel convolutional neural network (MCCNN) can extract the fluctuation characteristics of EV charging load at various time scales, while the temporal convolutional network (TCN) can build a time-series dependence between the fluctuation characteristics and the forecasted load. In addition, an additional BP network maps the selected meteorological and date features into a high-dimensional feature vector, which is spliced with the output of the TCN. According to experimental results employing urban charging station load data from a city in northern China, the proposed model is more accurate than artificial neural network (ANN), long short-term memory (LSTM), convolutional neural networks and long short-term memory (CNN-LSTM), and TCN models. The MCCNN-TCN model outperforms the ANN, LSTM, CNN-LSTM, and TCN by 14.09%, 25.13%, 27.32%, and 4.48%, respectively, in terms of the mean absolute percentage error.

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Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN

Zhang

Liu

2022

Energies

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“…(4) Stability will be disrupted, because the company's financial unit will be focused on the project at disburse additional fee to cover the cost of the underpayment and the exclusion of other projects. (5) Causes not optimal in the allocation of resources, improving efficiency, and increasing the company's revenue. From identification of problems exposed above, it may be obtained dimension of the problem is so vast.…”

Section: Introductionmentioning

confidence: 99%

Prediction Of Lamp Price Using Adaptive Neuro Fuzzy Inference System

Ridwan¹

2018

Proceedings of the the 1st International Conference on Computer Science and Engineering Technology Universitas Muria Kudus

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Precise Lamp predictions lead to the optimal allocation of resources, efficiency improvements, and increased company revenue. to forecast the price of building lights is a type of non-linear prediction. In this study, periodic data with time range of data from Jan-2009 to Dec-2015 used as prediction process parameter using ANFIS. The research method was started by mapping data into 4 input parameters and 1 output result, followed by training and testing 3 times with data classification period 1-19, 2-20, and 3-21. The result of the research Prediction of price using ANFIS model based on 63 data with 3 variant of light and input parameter that include number of membership function = 2, membership function type Trapezium, error goal 0, and epoch Maximum 100 able to give value of RMSE=0.05, MAPE=5.62, and produces predictive price according to actual data with percentage difference of 1,65x10-3.

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Forecasting US NASDAQ stock index values using hybrid forecasting systems

Banik

Khan

2015

2015 18th International Conference on Computer and Information Technology (ICCIT)

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A Hybrid Model Based on ANFIS and Empirical Mode Decomposition for Stock Forecasting

Cited by 3 publications

References 28 publications

Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN

Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN

Prediction Of Lamp Price Using Adaptive Neuro Fuzzy Inference System

Forecasting US NASDAQ stock index values using hybrid forecasting systems

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