Roll motion prediction using a hybrid deep learning and ARIMA model

Suhermi, Novri; Suhartono, Suhartono; Prastyo, Dedy Dwi; Ali, Baharuddin

doi:10.1016/j.procs.2018.10.526

Cited by 65 publications

(32 citation statements)

References 13 publications

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“…More and more people combine time series statistical models with neural network models, and use neural network models to process non-periodic components to improve accuracy. For example, the mixed model of ARIMA and ANN has obtained better prediction results [ 17 , 18 ]. The adaptive fuzzy neural network is used to predict the lane changing behavior of vehicles, and compared with the three traditional machine learning methods of neural network, support vector machine and multiple linear regression, the result is that AFNN has the best effect [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

A Time Series Data Filling Method Based on LSTM—Taking the Stem Moisture as an Example

Song

Gao

Zhao

et al. 2020

Sensors

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In order to solve the problem of data loss in sensor data collection, this paper took the stem moisture data of plants as the object, and compared the filling value of missing data in the same data segment with different data filling methods to verify the validity and accuracy of the stem water filling data of the LSTM (Long Short-Term Memory) model. This paper compared the accuracy of missing stem water data for plants under different data filling methods to solve the problem of data loss in sensor data collection. Original stem moisture data was selected from Lagerstroemia Indica which was planted in the Haidian District of Beijing in June 2017. Part of the data which treated as missing data was manually deleted. Interpolation methods, time series statistical methods, the RNN (Recurrent Neural Network), and LSTM neural network were used to fill in the missing part and the filling results were compared with the original data. The result shows that the LSTM has more accurate performance than the RNN. The error values of the bidirectional LSTM model are the smallest among several models. The error values of the bidirectional LSTM are much lower than other methods. The MAPE (mean absolute percent error) of the bidirectional LSTM model is 1.813%. After increasing the length of the training data, the results further proved the effectiveness of the model. Further, in order to solve the problem of one-dimensional filling error accumulation, the LSTM model is used to conduct the multi-dimensional filling experiment with environmental data. After comparing the filling results of different environmental parameters, three environmental parameters of air humidity, photosynthetic active radiation, and soil temperature were selected as input. The results show that the multi-dimensional filling can greatly extend the sequence length while maintaining the accuracy, and make up for the defect that the one-dimensional filling accumulates errors with the increase of the sequence. The minimum MAPE of multidimensional filling is 1.499%. In conclusion, the data filling method based on LSTM neural network has a great advantage in filling the long-lost time series data which would provide a new idea for data filling.

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Section: Introductionmentioning

confidence: 99%

A Time Series Data Filling Method Based on LSTM—Taking the Stem Moisture as an Example

Song

Gao

Zhao

et al. 2020

Sensors

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“…Table 5 shows the prediction performance of the PSO-IGM. 20 4.74 4.05 Figure 7 shows a graph summarizing the predictive performance of the IGM with contexts fixed to 5, 6, 7 and 8, respectively. As seen in the figure, the best prediction performance is obtained when the context is 7 and the number of clusters is 9.…”

Section: Results Analysismentioning

confidence: 99%

Incremental Granular Model Improvement Using Particle Swarm Optimization

Yeom

Kwak

2019

Symmetry

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This paper proposes an incremental granular model (IGM) based on particle swarm optimization (PSO) algorithm. An IGM is a combination of linear regression (LR) and granular model (GM) where the global part calculates the error using LR. However, traditional CFCM clustering presents some problems because the number of clusters generated in each context is the same and a fixed value is used for fuzzification coefficient. In order to solve these problems, we optimize the number of clusters and their fuzzy numbers according to the characteristics of the data, and use natural imitative optimization PSO algorithm. We further evaluate the performance of the proposed method and the existing IGM by comparing the predicted performance using the Boston housing dataset. The Boston housing dataset contains housing price information in Boston, USA, and features 13 input variables and 1 output variable. As a result of the prediction, we can confirm that the proposed PSO-IGM shows better performance than the existing IGM.

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“…Meanwhile, the adaptation of hybrid methodology combining ARIMA and Deep Neural Network (DNN), which is an ANN model with multiple hidden layers, was considered as the optimal model for predicting roll motion compared to the non-hybrid models. It was found out that DNN-ARIMA hybrid model showed improved forecast accuracy and was identified to be very effective [22]. 20, and 25 k-fold cross-validations and K-NN with k value assigned with 3, 5, and 7.…”

Section: Related Literaturementioning

confidence: 99%

Application of Time Series Analysis in Projecting Philippines’ Electric Consumption

Delima¹

2019

IJMLC

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This paper employed the famous ARIMA(p,d,q) model in forecasting Philippines' electric consumption for the years 2018 to 2022 using the univariate historical data of the country's power statistics in megawatt-hour from 2003-2017. The ACF and PACF plots were considered as well as stationarity of the data in assigning appropriate (p,d,q) values. By selecting the model with the lowest Akaike Information Criterion (AIC) value, an optimal model is then identified. The simulation result showed that ARIMA(1,2,1) model appeared to be the statistically appropriate model to forecast the Philippines' electrical consumption allocated for residential, commercial,

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Roll motion prediction using a hybrid deep learning and ARIMA model

Cited by 65 publications

References 13 publications

A Time Series Data Filling Method Based on LSTM—Taking the Stem Moisture as an Example

A Time Series Data Filling Method Based on LSTM—Taking the Stem Moisture as an Example

Incremental Granular Model Improvement Using Particle Swarm Optimization

Application of Time Series Analysis in Projecting Philippines’ Electric Consumption

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