A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting

Wei, Liang-Ying

doi:10.1016/j.asoc.2016.01.027

Cited by 173 publications

(74 citation statements)

References 29 publications

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“…Có nhiều mô hình dự báo đã được công bố trong thời gian gần đây như sử dụng cấu trúc cây TAN (Tree Augmented Naïve Bayes) [15], mạng neural ANN [3,14,21], mô hình SVM [6,28],… Trong các mô hình dự báo, hệ ANFIS phù hợp cho các bài toán có dữ liệu đầu vào phức tạp và dự báo được đồng thời nhiều kết quả khác nhau, ví dụ như dự báo giá cổ phiếu dựa trên Hamacher T-Norm nhiều đầu vào và ANFIS [29], dự báo giá đóng (close) [25], mô hình ANFIS áp dụng trên di động [2].…”

Section: Giới Thiệuunclassified

Hệ Anfis Cho Bài Toán Dự Báo Dữ Liệu Chuỗi Thời Gian Và Ứng Dụng

Hiền¹,

Hà²,

Anh³

et al. 2017

Fair - Nghiên Cứu Cơ Bản Và Ứng Dụng Công Nghệ Thông Tin - 2016

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Section: Giới Thiệuunclassified

Hệ Anfis Cho Bài Toán Dự Báo Dữ Liệu Chuỗi Thời Gian Và Ứng Dụng

Hiền¹,

Hà²,

Anh³

et al. 2017

Fair - Nghiên Cứu Cơ Bản Và Ứng Dụng Công Nghệ Thông Tin - 2016

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“…Para medir el desemepeño del modelo en cuanto a su capacidad de predicción, este fue comparado con el modelo Chen, el Yu, el modelo autoregresivo (AR), el modelo ANFIS y el modelo SVR. Los resultados mostraron que, teniendo en cuenta el error cuadrático medio, el modelo propuesto es superior a los otros (Wei, 2016).…”

Section: Algunos Casos Aplicadosunclassified

Modelo Log-Normal Para Predicción Del Precio De Las Acciones Del Sector Bancario // Log-Normal Model for Predicting the Price of Shares of the Banking Sector // Log-Normal Modelo De Predição Do Preço Das Ações Do Setor Bancário

Camargo¹,

Fontanilla²,

Luna³

2015

Dempresarial

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El objetivo de este artículo de investigación es la predicción de las cotizaciones de las acciones del sector bancario que cotizaron en el índice general de la bolsa de valores de Colombia (IGBC) durante el periodo 2008 – 2012. Para tal efecto, metodológicamente se hizo uso del modelo Log-normal complementado con simulaciones de Monte-Carlo a fin de determinar pruebas de bondad de ajuste del modelo mediante la raíz del error métrico cuadrado (RMSE). Los resultados encontrados indican que si bien es cierto el modelo sirve para tener una aproximación a los posibles valores mínimos y máximos que puede tomar las acciones, sus resultados carecen de la suficiente precisión para inducir la compra certera de este tipo de activo financiero, razón por la cual se recomienda en próximas investigaciones la aplicación de modelos con promedios móviles de suavizamiento exponencial y modelos de la familia Arch y Garch que generan mayor capacidad de predicción.

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“…The decomposed IMF components contain local characteristic signals with different time scales of the original signal. The IMF must meet the conditions that the difference between the extremum number and the zero number of the signal is 0 or 1 and the average value of the upper envelope and the lower envelope of the signal is zero [37][38][39]. For a given signal, the EMD steps are as follows:…”

Section: Mpso-lssvmmentioning

confidence: 99%

A Short-Term Load Forecasting Model with a Modified Particle Swarm Optimization Algorithm and Least Squares Support Vector Machine Based on the Denoising Method of Empirical Mode Decomposition and Grey Relational Analysis

Niu

Dai

2017

Energies

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Abstract:As an important part of power system planning and the basis of economic operation of power systems, the main work of power load forecasting is to predict the time distribution and spatial distribution of future power loads. The accuracy of load forecasting will directly influence the reliability of the power system. In this paper, a novel short-term Empirical Mode Decomposition-Grey Relational Analysis-Modified Particle Swarm Optimization-Least Squares Support Vector Machine (EMD-GRA-MPSO-LSSVM) load forecasting model is proposed. The model uses the de-noising method combining empirical mode decomposition and grey relational analysis to process the original load series and forecasts the processed subsequences by the algorithm of modified particle swarm optimization and least square support vector machine. Then, the final forecasting results can be obtained after reconstructing the forecasting series. This paper takes the Jibei area as an example to produce an empirical analysis for load forecasting. The model input includes the hourly load one week before the forecasting day and the daily maximum temperature, daily minimum temperature, daily average temperature, relative humidity, wind force, date type of the forecasting day. The model output is the hourly load of the forecasting day. The models of BP neural network, SVM (Support vector machine), LSSVM (Least squares support vector machine), PSO-LSSVM (Particle swarm optimization-Least squares support vector machine), MPSO-LSSVM (Modified particle swarm optimization-Least squares support vector machine), EMD-MPSO-LSSVM are selected to compare with the model of EMD-GRA-MPSO-LSSVM using the same sample. The comparison results verify that the short-term load forecasting model of EMD-GRA-MPSO-LSSVM proposed in this paper is superior to other models and has strong generalization ability and robustness. It can achieve good forecasting effect with high forecasting accuracy, providing a new idea and reference for accurate short-term load forecasting.

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A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting

Cited by 173 publications

References 29 publications

Hệ Anfis Cho Bài Toán Dự Báo Dữ Liệu Chuỗi Thời Gian Và Ứng Dụng

Hệ Anfis Cho Bài Toán Dự Báo Dữ Liệu Chuỗi Thời Gian Và Ứng Dụng

Modelo Log-Normal Para Predicción Del Precio De Las Acciones Del Sector Bancario // Log-Normal Model for Predicting the Price of Shares of the Banking Sector // Log-Normal Modelo De Predição Do Preço Das Ações Do Setor Bancário

A Short-Term Load Forecasting Model with a Modified Particle Swarm Optimization Algorithm and Least Squares Support Vector Machine Based on the Denoising Method of Empirical Mode Decomposition and Grey Relational Analysis

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