PERAMALAN INDEKS HARGA SAHAM MENGGUNAKAN ENSEMBLE EMPIRICAL MODE DECOMPOSITION (EEMD)

2021 ◽  
Vol 10 (2) ◽  
pp. 211-220
Author(s):  
Rosinar Siregar ◽  
Rukun Santoso ◽  
Puspita Kartikasari
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Stock Price ◽  
Price Index ◽  
Time Series Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Series Data ◽  
Polynomial Trend ◽  
Mode Decomposition ◽  
The Future

 Stock price fluctuations make investors tend to hesitate to invest in stock markets because of an uncertain situation in the future. One method that can solve these problems is to use forecasting about the stock prices in the future. Generally, the huge size of data non linear and non stationary, and it is difficult to be interpreted in concrete. This problem can be solved by performing the decomposition process. One of decomposition method in time series data is Ensemble Empirical Mode Decomposition (EEMD). EEMD is process decomposition data into several Intrinsic Mode Function (IMF) and the IMF residue. In this research, this concept applied to data Stock Price Index in Property, Real Estate, and Construction from July 1, 2019 to July 30, 2020 as many as 272 data. Based on the results of data processing, as many as 6 IMF and IMF remaining were used as IMF forecasting and the IMF remaining in the future. The forecast was performed by choosing the best model of each IMF component and IMF remaining, used ARIMA and polynomial trend. Keywords: Time Series Data, Stock Price Index, EEMD, ARIMA, Polynomial Trend.

A Hybrid Wind Speed Prediction Approach Based on Ensemble Empirical Mode Decomposition and BO-LSTM Neural Networks for Digital Twin

2020 ◽  
Author(s):  
Weifei Hu ◽  
Yihan He ◽  
Zhenyu Liu ◽  
Jianrong Tan ◽  
Ming Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Wind Speed ◽  
Empirical Mode Decomposition ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Ensemble Empirical Mode Decomposition ◽  
Series Data ◽  
Digital Twin ◽  
Mode Decomposition

Abstract Precise time series prediction serves as an important role in constructing a Digital Twin (DT). The various internal and external interferences result in highly non-linear and stochastic time series data sampled from real situations. Although artificial Neural Networks (ANNs) are often used to forecast time series for their strong self-learning and nonlinear fitting capabilities, it is a challenging and time-consuming task to obtain the optimal ANN architecture. This paper proposes a hybrid time series prediction model based on ensemble empirical mode decomposition (EEMD), long short-term memory (LSTM) neural networks, and Bayesian optimization (BO). To improve the predictability of stochastic and nonstationary time series, the EEMD method is implemented to decompose the original time series into several components, each of which is composed of single-frequency and stationary signal, and a residual signal. The decomposed signals are used to train the BO-LSTM neural networks, in which the hyper-parameters of the LSTM neural networks are fine-tuned by the BO algorithm. The following time series data are predicted by summating all the predictions of the decomposed signals based on the trained neural networks. To evaluate the performance of the proposed hybrid method (EEMD-BO-LSTM), this paper conducts a case study of wind speed time series prediction and has a comprehensive comparison between the proposed method and other approaches including the persistence model, ARIMA, LSTM neural networks, B0-LSTM neural networks, and EEMD-LSTM neural networks. Results show an improved prediction accuracy using the EEMD-BO-LSTM method by multiple accuracy metrics.

scholarly journals EMPIRICAL MODE DECOMPOSITION BASED ON THETA METHOD FOR FORECASTING DAILY STOCK PRICE

2020 ◽  
Vol 19 (Number 4) ◽  
pp. 533-558
Author(s):  
Mohammad Raquibul Hossain ◽  
Mohd Tahir Ismail
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Stock Price ◽  
Time Series Data ◽  
Stock Exchange ◽  
Training Data ◽  
Series Data ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Theta Method

Forecasting is a challenging task as time series data exhibit many features that cannot be captured by a single model. Therefore, many researchers have proposed various hybrid models in order to accommodate these features to improve forecasting results. This work proposed a hybrid method between Empirical Mode Decomposition (EMD) and Theta methods by considering better forecasting potentiality. Both EMD and Theta are efficient methods in their own ground of tasks for decomposition and forecasting, respectively. Combining them to obtain a better synergic outcome deserves consideration. EMD decomposed the training data from each of the five Financial Times Stock Exchange 100 Index (FTSE 100 Index) companies’ stock price time series data into Intrinsic Mode Functions (IMF) and residue. Then, the Theta method forecasted each decomposed subseries. Considering different forecast horizons, the effectiveness of this hybridisation was evaluated through values of conventional error measures found for test data and forecast data, which were obtained by adding forecast results for all component counterparts extracted from the EMD process. This study found that the proposed method produced better forecast accuracy than the other three classic methods and the hybrid EMD-ARIMA models.

Ensemble Empirical Mode Decomposition Based Deep Learning Model for Short-Term Wind Power Forecasting

2022 ◽  
Author(s):  
J.M. González-Sopeña
Keyword(s):  
Deep Learning ◽  
Wind Power ◽  
Empirical Mode Decomposition ◽  
Time Series Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Series Data ◽  
Intrinsic Mode Functions ◽  
Wind Power Forecasting ◽  
Mode Decomposition ◽  
Power Forecasting

Abstract. In the last few years, wind power forecasting has established itself as an essential tool in the energy industry due to the increase of wind power penetration in the electric grid. This paper presents a wind power forecasting method based on ensemble empirical mode decomposition (EEMD) and deep learning. EEMD is employed to decompose wind power time series data into several intrinsic mode functions and a residual component. Afterwards, every intrinsic mode function is trained by means of a CNN-LSTM architecture. Finally, wind power forecast is obtained by adding the prediction of every component. Compared to the benchmark model, the proposed approach provides more accurate predictions for several time horizons. Furthermore, prediction intervals are modelled using quantile regression.

scholarly journals Multiband Prediction Model for Financial Time Series with Multivariate Empirical Mode Decomposition

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Md. Rabiul Islam ◽  
Md. Rashed-Al-Mahfuz ◽  
Shamim Ahmad ◽  
Md. Khademul Islam Molla
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Time Series Data ◽  
Financial Time Series ◽  
Arma Model ◽  
Series Data ◽  
Discrete Wavelet ◽  
Multivariate Empirical Mode Decomposition ◽  
Financial Time ◽  
Mode Decomposition

This paper presents a subband approach to financial time series prediction. Multivariate empirical mode decomposition (MEMD) is employed here for multiband representation of multichannel financial time series together. Autoregressive moving average (ARMA) model is used in prediction of individual subband of any time series data. Then all the predicted subband signals are summed up to obtain the overall prediction. The ARMA model works better for stationary signal. With multiband representation, each subband becomes a band-limited (narrow band) signal and hence better prediction is achieved. The performance of the proposed MEMD-ARMA model is compared with classical EMD, discrete wavelet transform (DWT), and with full band ARMA model in terms of signal-to-noise ratio (SNR) and mean square error (MSE) between the original and predicted time series. The simulation results show that the MEMD-ARMA-based method performs better than the other methods.

scholarly journals Empirical Mode Decomposition Combined with Local Linear Quantile Regression for Automatic Boundary Correction

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Abobaker M. Jaber ◽  
Mohd Tahir Ismail ◽  
Alssaidi M. Altaher
Keyword(s):  
Time Series ◽  
Quantile Regression ◽  
Empirical Mode Decomposition ◽  
Time Series Data ◽  
Series Data ◽  
Second Stage ◽  
Mode Decomposition ◽  
Partial Data ◽  
Final Estimate ◽  
Practical Performance

Empirical mode decomposition (EMD) is particularly useful in analyzing nonstationary and nonlinear time series. However, only partial data within boundaries are available because of the bounded support of the underlying time series. Consequently, the application of EMD to finite time series data results in large biases at the edges by increasing the bias and creating artificial wiggles. This study introduces a new two-stage method to automatically decrease the boundary effects present in EMD. At the first stage, local polynomial quantile regression (LLQ) is applied to provide an efficient description of the corrupted and noisy data. The remaining series is assumed to be hidden in the residuals. Hence, EMD is applied to the residuals at the second stage. The final estimate is the summation of the fitting estimates from LLQ and EMD. Simulation was conducted to assess the practical performance of the proposed method. Results show that the proposed method is superior to classical EMD.

Toward a Digital Twin: Time Series Prediction Based on a Hybrid Ensemble Empirical Mode Decomposition and BO-LSTM Neural Networks

2020 ◽  
Vol 143 (5) ◽  
Author(s):  
Weifei Hu ◽  
Yihan He ◽  
Zhenyu Liu ◽  
Jianrong Tan ◽  
Ming Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Empirical Mode Decomposition ◽  
Time Series Prediction ◽  
Ensemble Empirical Mode Decomposition ◽  
Bayesian Optimization ◽  
Series Data ◽  
Digital Twin ◽  
Mode Decomposition ◽  
Highly Nonlinear

Abstract Precise time series prediction serves as an important role in constructing a digital twin (DT). The various internal and external interferences result in highly nonlinear and stochastic time series. Although artificial neural networks (ANNs) are often used to forecast time series because of their strong self-learning and nonlinear fitting capabilities, it is a challenging and time-consuming task to obtain the optimal ANN architecture. This paper proposes a hybrid time series prediction model based on an ensemble empirical mode decomposition (EEMD), long short-term memory (LSTM) neural networks, and Bayesian optimization (BO). To improve the predictability of stochastic and nonstationary time series, the EEMD method is implemented to decompose the original time series into several components (each component is a single-frequency and stationary signal) and a residual signal. The decomposed signals are used to train the neural networks, in which the hyperparameters are fine-tuned by the BO algorithm. The following time series data are predicted by summating all the predictions of the decomposed signals based on the trained neural networks. To evaluate the performance of the proposed EEMD-BO-LSTM neural networks, this paper conducts two case studies (the wind speed prediction and the wave height prediction) and implements a comprehensive comparison between the proposed method and other approaches including the persistence model, autoregressive integrated moving average (ARIMA) model, LSTM neural networks, BO-LSTM neural networks, and EEMD-LSTM neural networks. The results show an improved prediction accuracy using the proposed method by multiple accuracy metrics.

scholarly journals PENGARUH INFLASI, BI RATE, DAN NILAI TUKAR RUPIAH TERHADAP PERUBAHAN INDEKS HARGA SAHAM.SEKTOR CONSUMER GOODS

2021 ◽  
Vol 4 (1) ◽  
pp. 51-63
Author(s):  
Diah Budi Pratiwi ◽  
Damayanti Damayanti ◽  
M. Iqbal Iqbal Harori
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Consumer Goods ◽  
Stock Price ◽  
Price Index ◽  
Time Series Data ◽  
Series Data ◽  
A Value ◽  
Stock Price Index ◽  
Positive Effect

This research aims to find out the macroeconomic influence of inflation, bi rate, and rupiah exchange rate on changes in the stock price index of consumer goods sector. The independent variables that used in this research are Inflation (X1), BI Rate (X1), and Rupiah Exchange Rate (X3) and Consumer Goods Sector Stock Price Index as dependent variable. The data in this research is a time series data that includes inflation, BI Rate, and Rupiah exchange rate data for the period 2016-2020. The samples in this research amounted to 60 samples that taken by using census sampling techniques. The data in this research was analyzed by using multiple linear regressions with simultaneous variable results of Inflation, BI rate, and Rupiah Exchange Rate significantly affecting changes in the Consumer Goods Sector Stock Price Index with a value of R Square is 0.382 or 38.2%. While the results partially show that variable inflation has a significant and positive effect, variable rupiah exchange rates has negatively affect on changes in the Stock Price Index of the Consumer Goods Sector. As for the variable BI Rate has no significant effect on changes in the Stock Price Index of the Consumer Goods Sector. ABSTRAK   Penelitian ini bertujuan untuk mengetahui pengaruh ekonomi makro inflasi, bi rate, dan nilai tukar rupiah terhadap perubahan indeks harga saham sektor consumer goods. Variabel bebas yang digunakan pada penelitian ini yaitu Inflasi (X1), BI Rate (X1), dan Nilai Tukar Rupiah (X3) serta Indeks Harga Saham Sektor Consumer Goods sebagai variabel terikat. Data pada penelitian ini merupakan data time series yang meliputi data Inflasi, BI Rate, dan Nilai Tukar Rupiah untuk periode tahun 2016-2020. Sampel pada penelitian ini berjumlah 60 sampel yang diambil dengan menggunakan teknik sampling sensus. Data pada penelitian ini dianalisis dengan menggunakan regresi linier berganda, dengan hasil secara simultan, variabel Inflasi, BI rate, dan Nilai Tukar Rupiah berpengaruh signifikan terhadap perubahan Indeks Harga Saham Sektor Consumer Goods. Secara parsial, variabel inflasi berpengaruh signifikan dan positif, serta variabel nilai tukar rupiah berpengaruh negatif terhadap perubahan Indeks Harga Saham Sektor Consumer Goods. Sedangkan untuk variabel BI Rate tidak berpengaruh secara signifikan terhadap perubahan Indeks Harga Saham Sektor Consumer Goods.

Application of empirical mode decomposition (EMD) filtering at magnetotelluric time-series data

2020 ◽  
Author(s):  
Nugroho Syarif Setiawan ◽  
Amien Widodo ◽  
Wien Lestari ◽  
Firman Syaifuddin ◽  
Ahmad Zarkasyi ◽  
...  
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Time Series Data ◽  
Series Data ◽  
Mode Decomposition

scholarly journals ANALISIS FAKTOR PENENTU INDEKS HARGA SAHAM GABUNGAN DI INDONESIA

2019 ◽  
Vol 1 (4) ◽  
pp. 37
Author(s):  
Yulizar Fikri ◽  
Ali Anis
Keyword(s):  
Economic Growth ◽  
Foreign Exchange ◽  
Stock Price ◽  
Price Index ◽  
Time Series Data ◽  
Secondary Data ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Foreign Exchange Reserves ◽  
Stock Price Index

This study aims to determine the analysis of the determinants of the composite stock price index in Indonesia. The independent variables in this study are inflation as X1, foreign exchange reserves as X2, exchange rates as X3, and economic growth as X4, and the dependent variable of the composite stock price index as Y. The data used are secondary data in the formof time series data from 2010Q1 until 2019Q2, with data collection techniques, namely documentation from Bank Indonesia publications, the Central Statistics Agency, investing. comsite and library research. The research methods used are: (1) Multiple Linear Regression, (2) Classical Assumption Test (3) coefficient of determination. The results of this study indicate that:(1) inflation does not significantly influence the composite stock price index. (2) foreign exchange reserves have a significant positive effect on the composite stock price index. (3) the rupiah exchange rate has an influence on the composite stock price index and (4) economic growth hasno significant effect on the composite stock price index.

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