CEEMDAN-based financial time series combination prediction method

A financial time series and combined forecasting technology, applied in finance, reservations, instruments, etc., can solve the problems of difficult financial data forecasting and forecasting accuracy, cumbersome forecasting method process, etc., to achieve good adaptability, good performance, and accurate forecasting effect. Effect

Inactive Publication Date: 2019-10-11
SICHUAN UNIV
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Problems solved by technology

[0005] In view of the current situation and existing problems of the above-mentioned existing financial time series prediction methods, the present invention proposes a model combining CEEMDAN, LSTM and SVR to predict financial time

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  • CEEMDAN-based financial time series combination prediction method
  • CEEMDAN-based financial time series combination prediction method
  • CEEMDAN-based financial time series combination prediction method

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Embodiment Construction

[0010] The present invention will be further described in detail below in combination with specific embodiments.

[0011] 1. Through the CEEMDAN algorithm, the original financial time series Decomposed into several IMF subsequences and a remainder , and then divide all subsequences into test set and training set.

[0012] 2. Predictive model construction;

[0013] 1) Use LSTM unit to build a reliable time series prediction network, input IMF subsequence

[0014] The network consists of two layers of LSTM units and a layer of fully connected network. The abstract pattern in the time series is learned by using the two-layer LSTM network, which is the core part of the network. The structure of the network is as figure 2 As shown, since the output of the LSTM unit is a multidimensional vector, the output of the last LSTM unit of the second layer (layer2) network is connected to the fully connected network of the third layer (layer3). During the training process, the para...

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Abstract

The invention discloses a financial time series combination prediction method based on adaptive noise complete ensemble empirical mode decomposition (CEEMDAN). According to the method, firstly, a financial time sequence is decomposed into eigenmode function (IMF) sub-sequences and remainders through CEEMDAN; long-term short-term memory (LSTM) is combined with support vector regression (SVR) to construct prediction models which are respectively used for predicting IMF sub-sequences and remainders; and finally, each prediction sequence is reconstructed to obtain a prediction result. Experimentalresults show that the method is suitable for industrial production, single LSTM model, sVR model, compared with other mixed models, the prediction model shows better performance in short-term prediction of the financial time series, and the problems that an existing financial time series prediction method is tedious in process, difficult to directly predict nonlinear and non-stable financial dataand low in prediction accuracy are solved.

Description

technical field [0001] The invention belongs to the field of financial risk management and relates to a financial time series prediction method, in particular to a financial time series combination prediction method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) . Background technique [0002] Stock market price prediction has always been a field of great interest to professional researchers and investors. Stock price changes are nonlinear and non-stationary, and it is very difficult to predict price fluctuations reliably and accurately. From the perspective of forecasting research methods, there are mainly fundamental analysis and technical analysis. Fundamental analysis mainly predicts price trends based on information such as macroeconomic policies, industry development potential, and corporate profitability. Since this method is difficult to quantify, it is difficult to implement. The technical analysis method mainly relies on qu...

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Application Information

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IPC IPC(8): G06Q10/02G06Q40/04
CPCG06Q10/02G06Q40/04
Inventor 李智冯苗李健
Owner SICHUAN UNIV
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