scholarly journals PREDIKSI DATA TRANSAKSI PENJUALAN TIME SERIES MENGGUNAKAN REGRESI LSTM

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.

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

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.

scholarly journals Implementation of Long Short-Term Memory and Gated Recurrent Units on Grouped Time-Series Data to Predict Stock Prices Accurately

2021 ◽  
Author(s):  
Armin Lawi ◽  
Hendra Mesra ◽  
Supri Amir
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Percentage Error ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Abstract Stocks are an attractive investment option since they can generate large profits compared to other businesses. The movement of stock price patterns on the stock market is very dynamic; thus it requires accurate data modeling to forecast stock prices with a low error rate. Forecasting models using Deep Learning are believed to be able to accurately predict stock price movements using time-series data, especially the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. However, several previous implementation studies have not been able to obtain convincing accuracy results. This paper proposes the implementation of the forecasting method by classifying the movement of time-series data on company stock prices into three groups using LSTM and GRU. The accuracy of the built model is evaluated using loss functions of Rooted Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results showed that the performance evaluation of both architectures is accurate in which GRU is always superior to LSTM. The highest validation for GRU was 98.73% (RMSE) and 98.54% (MAPE), while the LSTM validation was 98.26% (RMSE) and 97.71% (MAPE).

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

Electronics ◽  
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.

scholarly journals Development of a Model for Predicting the Direction of Daily Price Changes in the Forex Market Using Long Short-Term Memory

2021 ◽  
Vol 11 (1) ◽  
pp. 61-67
Author(s):  
Watthana Pongsena ◽  
◽  
Prakaidoy Sitsayabut ◽  
Nittaya Kerdprasop ◽  
Kittisak Kerdprasop ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Short Term Memory ◽  
Series Data ◽  
Short Term ◽  
Price Changes ◽  
Term Memory ◽  
The Future ◽  
Long Short Term Memory

Forex is the largest global financial market in the world. Traditionally, fundamental and technical analysis are strategies that the Forex traders often used. Nowadays, advanced computational technology, Artificial Intelligence (AI) has played a significant role in the financial domain. Various applications based on AI technologies particularly machine learning and deep learning have been constantly developed. As the historical data of the Forex are time-series data where the values from the past affect the values that will appear in the future. Several existing works from other domains of applications have proved that the Long-Short Term Memory (LSTM), which is a particular kind of deep learning that can be applied to modeling time series, provides better performance than traditional machine learning algorithms. In this paper, we aim to develop a powerful predictive model targeting to predicts the daily price changes of the currency pairwise in the Forex market using LSTM. Besides, we also conduct an extensive experiment with the intention to demonstrate the effect of various factors contributing to the performance of the model. The experimental results show that the optimized LSTM model accurately predicts the direction of the future price up to 61.25 percent.

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

Forests ◽  
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.

scholarly journals Forecasting number of vulnerabilities using long short-term neural memory network

2021 ◽  
Vol 11 (5) ◽  
pp. 4381
Author(s):  
Mohammad Shamsul Hoque ◽  
Norziana Jamil ◽  
Nowshad Amin ◽  
Azril Azam Abdul Rahim ◽  
Razali B. Jidin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Time Series ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Data Points ◽  
Memory Network ◽  
Past Data ◽  
Long Short Term Memory

Cyber-attacks are launched through the exploitation of some existing vulnerabilities in the software, hardware, system and/or network. Machine learning algorithms can be used to forecast the number of post release vulnerabilities. Traditional neural networks work like a black box approach; hence it is unclear how reasoning is used in utilizing past data points in inferring the subsequent data points. However, the long short-term memory network (LSTM), a variant of the recurrent neural network, is able to address this limitation by introducing a lot of loops in its network to retain and utilize past data points for future calculations. Moving on from the previous finding, we further enhance the results to predict the number of vulnerabilities by developing a time series-based sequential model using a long short-term memory neural network. Specifically, this study developed a supervised machine learning based on the non-linear sequential time series forecasting model with a long short-term memory neural network to predict the number of vulnerabilities for three vendors having the highest number of vulnerabilities published in the national vulnerability database (NVD), namely microsoft, IBM and oracle. Our proposed model outperforms the existing models with a prediction result root mean squared error (RMSE) of as low as 0.072.

scholarly journals Prediksi Data Time Series Saham Bank BRI Dengan Mesin Belajar LSTM (Long ShortTerm Memory)

2020 ◽  
Vol 1 (1) ◽  
pp. 1-8
Author(s):  
Adhitio Satyo Bayangkari Karno
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Short Term Memory ◽  
Mean Square ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Abstract   This study aims to measure the accuracy in predicting time series data using the LSTM (Long Short-Term Memory) machine learning method, and determine the number of epochs needed to produce a small RMSE (Root Mean Square Error) value. The result of this research is a high level of variation in RMSE value to the number of epochs needed in the data processing. This variation is quite difficult to obtain the right epoch value. By doing an iteration of the LSTM process on the number of different epochs (visualized in the graph), then the number of epochs with a minimum RMSE value will be easier to obtain. From the research of BBRI's stock data prediction, a good RMSE value was obtained (RMSE = 227.470333244533).   Keywords: long short-term memory, machine learning, epoch, root mean square error, mean square error.   Abstrak   Penelitian ini bertujuan untuk mengukur ketelitian dalam memprediksi data time series menggunakan metode mesin belajar LSTM (Long Short-Term Memory), serta menentukan banyaknya epoch yang diperlukan untuk menghasilkan nilai RMSE (Root Mean Square Error) yang kecil. Hasil dari penelitian ini adalah tingkat variasi yang tinggi nilai rmse terhdap jumlah epoch yang diperlukan dalam proses pengolahan data. Variasi ini cukup menyulitkan untuk memperoleh nilai epoch yang tepat. Dengan melakukan iterasi dari proses LSTM terhadap jumlah epoch yang berbeda (di visualisasikan dalam grafik), maka jumlah epoch dengan nilai RMSE minimal akan lebih mudah diperoleh. Dari penelitan prediksi data saham  BBRI diperoleh nilai RMSE yang cukup baik yaitu 227,470333244533. Kata kunci: long short-term memory, machine learning, epoch, root mean square error, mean square error.

scholarly journals Prediksi Penjualan Supermarket Menggunakan Pendekatan Deep Learning

2021 ◽  
Vol 7 (1) ◽  
pp. 160
Author(s):  
Marchel Thimoty Tombeng ◽  
Zalfie Ardian
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Short Term Memory ◽  
Time Series Model ◽  
Time Machine ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Berdasarkan data transaksi tahun 2014 sampai 2016 dari salah satu supermarket yang ada di Taiwan, penulis menghasilkan analisa model prediksi dengan menguji data menggunakan metode Deep Learning. Beberapa faktor yang berpengaruh telah di dipelajari dan berguna untuk input prediksi, antara lain keadaan cuacu, diskon, hari raya, dan lain sebagainya. Motivasi utama dari penelitian yang penulis lakukan adalah menggunakan teknologi yang berhubungan dengan eksplorasi data untuk memprediksikan penjualan dari produk-produk dan waktu berkunjung pelangan dalam industry retail, untuk mencari grup target yang tepat dan korelasi produk yang tinggi. Pada akhirnya penulis menciptakan sistem keputusan produk yang berisi analisa visual dan tindakan saran untuk manajer produk pemasaran serta pemangku kepentingan dalam pemasaran produk. Dengan adanya hasil prediksi ini, diharapkan dapat menbantu manajer atau pemangku kepentingan lainnya untuk dapat memasarkan serta menjual produk secara tepat sehingga dapat menghasilkan keuntungan yang banyak dengan menggunkan analisa prediksi yang kami buat. LSTM merupakan model yang sering dipakai dalam Recursive Neural Network (RNN), dan pada dasarnya berfungsi untuk memecahkan masalah dari Time Series. Model Deep Learning yang penulis gunakan adalah Long Short Term Memory (LSTM), dimana model ini menyediakan analisa dan prediksi dari serangkaian data. Sebagai contoh, pada saat akhir pekan pengunjungnya melonjat, maka time machine learning ini akan menambahkan pengartian dari nilai parameter akhir pekan dan nilai ouputnya memiliki korelasi yang kuat.Kata kunci—Predictions, Time Series, LSTM, RNN, Deep Learning

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

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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