PREDICTION OF INCOMING ORDERS USING THE LONG SHORT-TERM MEMORY METHOD AT PT. XYZ

Currently the need for domestic packaging paper continues to increase, driven by the level of consumer awareness about sustainable packaging. PT XYZ is a local company engaged in the Corrugated Cardboard Box (KKG) industry. So far, the problems in fulfilling incoming orders every month are not optimal with an average of about 30% inaccuracy. This is because the orders that enter cannot be predicted. As an effort to win market competition in packaging paper, PT. XYZ must improve the fulfillment of incoming orders by predicting incoming orders using the Long Short-Term Memory (LSTM) method. The aim of this research is to provide a predictive model for incoming orders in accordance with the needs of order fulfillment to be applied to production planning. So that order fulfillment can be on time. The method used in predicting incoming orders is the Long Short-Term Memory (LSTM) method using weighting evaluations with the lowest Root Mean Squared Error (RMSE) and Augmented Dickey-Fuller test (ADF). The test results of the LSTM method with parameter sizes of Batch: 1 Epochs: 5000 Neurons: 1 show that the RMSE for MDM products is 8.767582 and 0.287924, LNR products are 10.623984 and 0.466621, WTP products are 1.636849 and 0.361515 lower than the size of the fit parameters for other LSTM models, and the ADF Statistic value for MDM products -6.137597, LNR -6.753697, WTP -4.872927

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A Three-Stage Data-Driven Approach for Determining Reaction Wheels’ Remaining Useful Life Using Long Short-Term Memory

Electronics ◽

10.3390/electronics10192432 ◽

2021 ◽

Vol 10 (19) ◽

pp. 2432

Author(s):

Md Sirajul Islam ◽

Afshin Rahimi

Keyword(s):

System Parameter ◽

Short Term Memory ◽

Mean Squared Error ◽

Remaining Useful Life ◽

Health Index ◽

Short Term ◽

Term Memory ◽

Squared Error ◽

Useful Life ◽

Long Short Term Memory

Reaction wheels are widely used in the attitude control system of small satellites. Unfortunately, reaction wheels failure restricts the efficacy of a satellite, and it is one of the many reasons leading to premature abandonment of the satellites. This study observes the measurable system parameter of a faulty reaction wheel induced with incipient fault to estimate the remaining useful life of the reaction wheels. We achieve this goal in three stages, as none of the observable system parameters are directly related to the health of a reaction wheel. In the first stage, we identify the necessary observable system parameter and predict the future of these parameters using sensor acquired data and a long short-term memory recurrent neural network. In the second stage, we estimate the health index parameter using a multivariate long short-term memory network. In the third stage, we predict the remaining useful life of reaction wheels based on historical data of the health index parameter. Normalized root mean squared error is used to evaluate the performance of the various models in each stage. Additionally, three different timespans (short, moderate, and extended in the scale of small satellite orbit times) are simulated and tested for the performance of the proposed methodology regarding the malfunction of reaction wheels. Furthermore, the robustness of the proposed method to missing values, input frequency, and noise is studied. The results show promising performance for the proposed scheme with accuracy in predicting health index parameter around 0.01–0.02 normalized root mean squared error, the accuracy in prediction of RUL of 1%–2.5%, and robustness to various uncertainty factors, as discussed above.

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CL-Net: ConvLSTM-Based Hybrid Architecture for Batteries’ State of Health and Power Consumption Forecasting

Mathematics ◽

10.3390/math9243326 ◽

2021 ◽

Vol 9 (24) ◽

pp. 3326

Author(s):

Noman Khan ◽

Ijaz Ul Haq ◽

Fath U Min Ullah ◽

Samee Ullah Khan ◽

Mi Young Lee

Keyword(s):

Power Consumption ◽

Short Term Memory ◽

Mean Squared Error ◽

Renewable Energy Sources ◽

The State ◽

Hybrid Architecture ◽

State Of Health ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

Traditional power generating technologies rely on fossil fuels, which contribute to worldwide environmental issues such as global warming and climate change. As a result, renewable energy sources (RESs) are used for power generation where battery energy storage systems (BESSs) are widely used to store electrical energy for backup, match power consumption and generation during peak hours, and promote energy efficiency in a pollution-free environment. Accurate battery state of health (SOH) prediction is critical because it plays a key role in ensuring battery safety, lowering maintenance costs, and reducing BESS inconsistencies. The precise power consumption forecasting is critical for preventing power shortage and oversupply, and the complicated physicochemical features of batteries dilapidation cannot be directly acquired. Therefore, in this paper, a novel hybrid architecture called ‘CL-Net’ based on convolutional long short-term memory (ConvLSTM) and long short-term memory (LSTM) is proposed for multi-step SOH and power consumption forecasting. First, battery SOH and power consumption-related raw data are collected and passed through a preprocessing step for data cleansing. Second, the processed data are fed into ConvLSTM layers, which extract spatiotemporal features and form their encoded maps. Third, LSTM layers are used to decode the encoded features and pass them to fully connected layers for final multi-step forecasting. Finally, a comprehensive ablation study is conducted on several combinations of sequential learning models using three different time series datasets, i.e., national aeronautics and space administration (NASA) battery, individual household electric power consumption (IHEPC), and domestic energy management system (DEMS). The proposed CL-Net architecture reduces root mean squared error (RMSE) up to 0.13 and 0.0052 on the NASA battery and IHEPC datasets, respectively, compared to the state-of-the-arts. These experimental results show that the proposed architecture can provide robust and accurate SOH and power consumption forecasting compared to the state-of-the-art.

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Data Preparation and Training Methodology for Modeling Lithium-Ion Batteries Using a Long Short-Term Memory Neural Network for Mild-Hybrid Vehicle Applications

Applied Sciences ◽

10.3390/app10217880 ◽

2020 ◽

Vol 10 (21) ◽

pp. 7880

Author(s):

Daniel Jerouschek ◽

Ömer Tan ◽

Ralph Kennel ◽

Ahmet Taskiran

Keyword(s):

Neural Network ◽

Lithium Ion Batteries ◽

Short Term Memory ◽

Mean Squared Error ◽

Lithium Ion ◽

Dense Layer ◽

Intermediate Step ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

Voltage models of lithium-ion batteries (LIB) are used to estimate their future voltages, based on the assumption of a specific current profile, in order to ensure that the LIB remains in a safe operation mode. Data of measurable physical features—current, voltage and temperature—are processed using both over- and undersampling methods, in order to obtain evenly distributed and, therefore, appropriate data to train the model. The trained recurrent neural network (RNN) consists of two long short-term memory (LSTM) layers and one dense layer. Validation measurements over a wide power and temperature range are carried out on a test bench, resulting in a mean absolute error (MAE) of 0.43 V and a mean squared error (MSE) of 0.40 V2. The raw data and modeling process can be carried out without any prior knowledge of LIBs or the tested battery. Due to the challenges involved in modeling the state-of-charge (SOC), measurements are used directly to model the behavior without taking the SOC estimation as an input feature or calculating it in an intermediate step.

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Analysis of Lumber Prices Time Series Using Long Short-Term Memory Artificial Neural Networks

Forests ◽

10.3390/f12040428 ◽

2021 ◽

Vol 12 (4) ◽

pp. 428

Author(s):

Dercilio Junior Verly Lopes ◽

Gabrielly dos Santos Bobadilha ◽

Amanda Peres Vieira Bedette

Keyword(s):

Stock Prices ◽

Short Term Memory ◽

Mean Squared Error ◽

Absolute Error ◽

Short Term ◽

Term Memory ◽

Squared Error ◽

Dense Output ◽

The Usa ◽

Long Short Term Memory

This manuscript confirms the feasibility of using a long short-term memory (LSTM) recurrent neural network (RNN) to forecast lumber stock prices during the great and Coronavirus disease 2019 (COVID-19) pandemic recessions in the USA. The database was composed of 5012 data entries divided into recession periods. We applied a timeseries cross-validation that divided the dataset into an 80:20 training/validation ratio. The network contained five LSTM layers with 50 units each followed by a dense output layer. We evaluated the performance of the network via mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) for 30, 60, and 120 timesteps and the recession periods. The metrics results indicated that the network was able to capture the trend for both recession periods with a remarkably low degree of error. Timeseries forecasting may help the forest and forest product industries to manage their inventory, transportation costs, and response readiness to critical economic events.

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PREDIKSI DATA TRANSAKSI PENJUALAN TIME SERIES MENGGUNAKAN REGRESI LSTM

Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) ◽

10.23887/janapati.v9i1.19140 ◽

2020 ◽

Vol 9 (1) ◽

pp. 1

Author(s):

Marie Luthfi Ashari ◽

Mujiono Sadikin

Keyword(s):

Machine Learning ◽

Time Series ◽

Short Term Memory ◽

Mean Squared Error ◽

Percentage Error ◽

Short Term ◽

Term Memory ◽

Root Mean Squared Error ◽

Squared Error ◽

Long Short Term Memory

Sebagai upaya untuk memenangkan persaingan di pasar, perusahaan farmasi harus menghasilkan produk obat – obatan yang berkualitas. Untuk menghasilkan produk yang berkualitas, diperlukan perencanaan produksi yang baik dan efisien. Salah satu dasar perencanaan produksi adalah prediksi penjualan. PT. Metiska Farma telah menerapkan metode prediksi dalam proses produksi, akan tetapi prediksi yang dihasilkan tidak akurat sehingga menyebabkan tidak optimal dalam memenuhi permintaan pasar. Untuk meminimalisir masalah kurang akuratnya proses prediksi tersebut, dalam penelitian yang disajikan pada makalah ini dilakukan uji coba prediksi menggunakan teknik Machine Learning dengan metode Regresi Long Short Term Memory (LSTM). Teknik yang diusulkan diuji coba menggunakan dataset penjualan produk “X” dari PT. Metiska Farma dengan parameter kinerja Root Mean Squared Error (RMSE) dan MAPE (Mean Absolute Percentage Error). Hasil penelitian ini berupa nilai rata – rata evaluasi error dari pemodelan data training dan data testing. Di mana hasil menunjukan bahwa Regresi LSTM memiliki nilai prediksi penjualan dengan evaluasi model melalui RMSE sebesar 286.465.424 untuk data training dan 187.013.430 untuk data testing. Untuk nilai MAPE sebesar 787% dan 309% untuk data training dan data testing secara berurut.

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Application of Long Short Term Memory to Predict Flight Delay on Commercial Flights

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v4i3.1759 ◽

2020 ◽

Vol 4 (3) ◽

pp. 447-453

Author(s):

Muhammad Genta Ari Shandi ◽

Rifki Adhitama ◽

Amalia Beladinna Arifa

Keyword(s):

Short Term Memory ◽

Mean Squared Error ◽

Short Term ◽

Term Memory ◽

Root Mean Squared Error ◽

Squared Error ◽

Long Short Term Memory ◽

Flight Delay ◽

Unpleasant Experience ◽

Better Than

Delay in airline services, become an unpleasant experience for passengers who experience it. This study aims to build a model that can predict flight delay (departure) using the Long Short Term Memory method and can find out its performance. In this study there are two scenarios that have different ways of preprocessing. Both of these scenarios produce predictions with error values calculated using Root Mean Squared Error (RMSE), respectively from the first to the second scenario namely: 41, 21. Between the two, the second scenario is better than the first scenario due to extreme data deletion ( anomaly) in the second scenario with an error value using RMSE of 0.116.

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PREDIKSI HARGA FOREIGN EXCHANGE MATA UANG EUR/USD DAN GBP/USD MENGGUNAKAN LONG SHORT-TERM MEMORY

Sainsbertek Jurnal Ilmiah Sains & Teknologi ◽

10.33479/sb.v2i1.121 ◽

2021 ◽

Vol 2 (1) ◽

pp. 16-31

Author(s):

Angel Joanna Wijaya ◽

Windra Swastika ◽

Oesman Hendra Kelana

Keyword(s):

Foreign Exchange ◽

Short Term Memory ◽

Mean Squared Error ◽

Short Term ◽

Term Memory ◽

Squared Error ◽

Long Short Term Memory

Foreign exchange (Forex) adalah perdagangan pasangan mata uang dari harga mata uang suatu negara terhadap mata uang negara lainnya. Pada penelitian ini menggunakan metode Long Short-Term Memory (LSTM) untuk memprediksi harga close mata uang EUR/USD (Euro terhadap Dolar Amerika) dan GBP/USD (Pound Sterling terhadap Dolar Amerika) pada candle D1 (1 hari) dengan input harga open dan close. Hasil yang diperoleh model EUR/USD dengan 1 input mendapatkan nilai Mean Squared Error (MSE) terendah yaitu 0,0535 dengan model 1 layer LSTM 10 node dan menggunakan optimizer Nadam. Pada model 3 input mendapatkan nilai MSE terendah yaitu 0,0529 dengan model 1 layer LSTM 10 node dan menggunakan optimizer Nadam. Sedangkan, model 5 input mendapatkan nilai MSE terendah yaitu 0,0469 dengan model 1 layer LSTM 10 node dan menggunakan optimizer Nadam. Pada mata uang GBP/USD, model dengan 1 input mendapatkan nilai MSE terendah yaitu 0,0543 dengan model 1 layer LSTM 10 node dan menggunakan optimizer Nadam. Pada model 3 input mendapatkan nilai MSE terendah yaitu 0,0520 dengan model 1 layer LSTM 10 node dan menggunakan optimizer Adam. Sedangkan, model 5 input mendapatkan nilai MSE terendah yaitu 0,0631 dengan model 1 layer LSTM 10 node dan menggunakan optimizer Adam. Model dengan MSE terbaik yang digunakan pada website yang dibuat. Rekomendasi yang diberikan adalah pada mata uang EUR/USD menggunakan model 5 input dan mata uang GBP/USD menggunakan model 3 input. Hasil loss MSE, akurasi MSE, dan jumlah profit yang didapatkan semuanya adalah yang terbaik jika dibandingkan dengan model dengan jumlah input lainnya

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A Stock Closing Price Prediction Model Based on CNN-BiSLSTM

Complexity ◽

10.1155/2021/5360828 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Haiyao Wang ◽

Jianxuan Wang ◽

Lihui Cao ◽

Yifan Li ◽

Qiuhong Sun ◽

...

Keyword(s):

Neural Network ◽

Stock Market ◽

Stock Price ◽

Short Term Memory ◽

Mean Squared Error ◽

Absolute Error ◽

Short Term ◽

Term Memory ◽

Closing Price ◽

Long Short Term Memory

As the stock market is an important part of the national economy, more and more investors have begun to pay attention to the methods to improve the return on investment and effectively avoid certain risks. Many factors affect the trend of the stock market, and the relevant information has the nature of time series. This paper proposes a composite model CNN-BiSLSTM to predict the closing price of the stock. Bidirectional special long short-term memory (BiSLSTM) improved on bidirectional long short-term memory (BiLSTM) adds 1 − tanh(x) function in the output gate which makes the model better predict the stock price. The model extracts advanced features that influence stock price through convolutional neural network (CNN), and predicts the stock closing price through BiSLSTM after the data processed by CNN. To verify the effectiveness of the model, the historical data of the Shenzhen Component Index from July 1, 1991, to October 30, 2020, are used to train and test the CNN-BiSLSTM. CNN-BiSLSTM is compared with multilayer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), BiLSTM, CNN-LSTM, and CNN-BiLSTM. The experimental results show that the mean absolute error (MAE), root-mean-squared error (RMSE), and R-square (R2) evaluation indicators of the CNN-BiSLSTM are all optimal. Therefore, CNN-BiSLSTM can accurately predict the closing price of the Shenzhen Component Index of the next trading day, which can be used as a reference for the majority of investors to effectively avoid certain risks.

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