scholarly journals Time Series Forecasting of Agricultural Products’ Sales Volumes Based on Seasonal Long Short-Term Memory

2020 ◽  
Vol 10 (22) ◽  
pp. 8169
Author(s):  
Tae-Woong Yoo ◽  
Il-Seok Oh
Keyword(s):  
Time Series ◽  
Performance Metrics ◽  
Short Term Memory ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Agricultural Products ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

In this paper, we propose seasonal long short-term memory (SLSTM), which is a method for predicting the sales of agricultural products, to stabilize supply and demand. The SLSTM model is trained using the seasonality attributes of week, month, and quarter as additional inputs to historical time-series data. The seasonality attributes are entered into the SLSTM network model individually or in combination. The performance of the proposed SLSTM model was compared with those of auto_arima, Prophet, and a standard LSTM in terms of three performance metrics (mean absolute error (MAE), root mean squared error (RMSE), and normalization mean absolute error (NMAE)). The experimental results show that the error rate of the proposed SLSTM model is significantly lower than those of other classical methods.

scholarly journals Long Short Term Memory Recurrent Network for Standard and Poor’s 500 Index Modelling

2018 ◽  
Vol 7 (4.15) ◽  
pp. 25 ◽  
Author(s):  
Said Jadid Abdulkadir ◽  
Hitham Alhussian ◽  
Muhammad Nazmi ◽  
Asim A Elsheikh
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recurrent Neural Network ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Forecasting time-series data are imperative especially when planning is required through modelling using uncertain knowledge of future events. Recurrent neural network models have been applied in the industry and outperform standard artificial neural networks in forecasting, but fail in long term time-series forecasting due to the vanishing gradient problem. This study offers a robust solution that can be implemented for long-term forecasting using a special architecture of recurrent neural network known as Long Short Term Memory (LSTM) model to overcome the vanishing gradient problem. LSTM is specially designed to avoid the long-term dependency problem as their default behavior. Empirical analysis is performed using quantitative forecasting metrics and comparative model performance on the forecasted outputs. An evaluation analysis is performed to validate that the LSTM model provides better forecasted outputs on Standard & Poor’s 500 Index (S&P 500) in terms of error metrics as compared to other forecasting models.  

Deep Network based on Long Short-Term Memory for Time Series Prediction of Microclimate Data inside the Greenhouse

2020 ◽  
Vol 19 (02) ◽  
pp. 2050013
Author(s):  
Sawsan Morkos Gharghory
Keyword(s):  
Time Series ◽  
Relative Humidity ◽  
Time Series Data ◽  
Short Term Memory ◽  
Weather Data ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Lstm Network

An enhanced architecture of recurrent neural network based on Long Short-Term Memory (LSTM) is suggested in this paper for predicting the microclimate inside the greenhouse through its time series data. The microclimate inside the greenhouse largely affected by the external weather variations and it has a great impact on the greenhouse crops and its production. Therefore, it is a massive importance to predict the microclimate inside greenhouse as a preceding stage for accurate design of a control system that could fulfill the requirements of suitable environment for the plants and crop managing. The LSTM network is trained and tested by the temperatures and relative humidity data measured inside the greenhouse utilizing the mathematical greenhouse model with the outside weather data over 27 days. To evaluate the prediction accuracy of the suggested LSTM network, different measurements, such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), are calculated and compared to those of conventional networks in references. The simulation results of LSTM network for forecasting the temperature and relative humidity inside greenhouse outperform over those of the traditional methods. The prediction results of temperature and humidity inside greenhouse in terms of RMSE approximately are 0.16 and 0.62 and in terms of MAE are 0.11 and 0.4, respectively, for both of them.

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 Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sujan Ghimire ◽  
Zaher Mundher Yaseen ◽  
Aitazaz A. Farooque ◽  
Ravinesh C. Deo ◽  
Ji Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Flood Control ◽  
Performance Metrics ◽  
Short Term Memory ◽  
Short Term ◽  
Neural Network Models ◽  
Time Intervals ◽  
Term Memory ◽  
Long Short Term Memory

AbstractStreamflow (Qflow) prediction is one of the essential steps for the reliable and robust water resources planning and management. It is highly vital for hydropower operation, agricultural planning, and flood control. In this study, the convolution neural network (CNN) and Long-Short-term Memory network (LSTM) are combined to make a new integrated model called CNN-LSTM to predict the hourly Qflow (short-term) at Brisbane River and Teewah Creek, Australia. The CNN layers were used to extract the features of Qflow time-series, while the LSTM networks use these features from CNN for Qflow time series prediction. The proposed CNN-LSTM model is benchmarked against the standalone model CNN, LSTM, and Deep Neural Network models and several conventional artificial intelligence (AI) models. Qflow prediction is conducted for different time intervals with the length of 1-Week, 2-Weeks, 4-Weeks, and 9-Months, respectively. With the help of different performance metrics and graphical analysis visualization, the experimental results reveal that with small residual error between the actual and predicted Qflow, the CNN-LSTM model outperforms all the benchmarked conventional AI models as well as ensemble models for all the time intervals. With 84% of Qflow prediction error below the range of 0.05 m3 s−1, CNN-LSTM demonstrates a better performance compared to 80% and 66% for LSTM and DNN, respectively. In summary, the results reveal that the proposed CNN-LSTM model based on the novel framework yields more accurate predictions. Thus, CNN-LSTM has significant practical value in Qflow prediction.

Deep machine learning for detection of acoustic wave reflections

2019 ◽  
Vol 19 (5) ◽  
pp. 1340-1350
Author(s):  
Mulugeta A Haile ◽  
Edward Zhu ◽  
Christopher Hsu ◽  
Natasha Bradley
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Acoustic Emission ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Propagating Waves ◽  
Long Short Term Memory

Acoustic emission signals are information rich and can be used to estimate the size and location of damage in structures. However, many existing algorithms may be deceived by indirectly propagated acoustic emission waves which are modulated by reflection boundaries within the structures. We propose two deep learning models to identify such waves such that existing algorithms for damage detection and localization may be used. The first approach uses long short-term memory recurrent neural networks to learn distinct patterns directly from the time-series data. In the second approach, we transform the time-series data into spectrograms and utilize convolutional neural networks to perform binary classification by leveraging spectro-temporal features. We achieved 80% classification accuracy using long short-term memory and near-perfect accuracy using convolutional neural networks on a dataset of acoustic emission signals generated by the Hsu-Nielsen sources. Both long short-term memory and convolutional neural network models were able to learn general and context-specific features of the direct and reflected acoustic emission waves. Once accurately identified, the indirectly propagating waves are filtered out while the directly propagating waves are used for source location using existing methods.

scholarly journals Previsão de casos de dengue através de Machine Learning e Deep Learning: uma revisão sistemática

2021 ◽  
Vol 10 (11) ◽  
pp. e33101119347
Author(s):  
Ewethon Dyego de Araujo Batista ◽  
Wellington Candeia de Araújo ◽  
Romeryto Vieira Lira ◽  
Laryssa Izabel de Araujo Batista
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Short Term Memory ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Support Vector ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
De Medicina

Introdução: a dengue é uma arbovirose causada pelo vírus DENV e transmitida para o homem através do mosquito Aedes aegypti. Atualmente, não existe uma vacina eficaz para combater todas as sorologias do vírus. Diante disso, o combate à doença se volta para medidas preventivas contra a proliferação do mosquito. Os pesquisadores estão utilizando Machine Learning (ML) e Deep Learning (DL) como ferramentas para prever casos de dengue e ajudar os governantes nesse combate. Objetivo: identificar quais técnicas e abordagens de ML e de DL estão sendo utilizadas na previsão de dengue. Métodos: revisão sistemática realizada nas bases das áreas de Medicina e de Computação com intuito de responder as perguntas de pesquisa: é possível realizar previsões de casos de dengue através de técnicas de ML e de DL, quais técnicas são utilizadas, onde os estudos estão sendo realizados, como e quais dados estão sendo utilizados? Resultados: após realizar as buscas, aplicar os critérios de inclusão, exclusão e leitura aprofundada, 14 artigos foram aprovados. As técnicas Random Forest (RF), Support Vector Regression (SVR), e Long Short-Term Memory (LSTM) estão presentes em 85% dos trabalhos. Em relação aos dados, na maioria, foram utilizados 10 anos de dados históricos da doença e informações climáticas. Por fim, a técnica Root Mean Absolute Error (RMSE) foi a preferida para mensurar o erro. Conclusão: a revisão evidenciou a viabilidade da utilização de técnicas de ML e de DL para a previsão de casos de dengue, com baixa taxa de erro e validada através de técnicas estatísticas.

scholarly journals Prediksi Indeks BEI dengan Ensemble Convolutional Neural Network dan Long Short-Term Memory

2021 ◽  
Vol 5 (3) ◽  
pp. 456-465
Author(s):  
Harya Widiputra ◽  
Adele Mailangkay ◽  
Elliana Gautama
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Stock Market Index ◽  
Short Term ◽  
Term Memory ◽  
Market Index ◽  
Long Short Term Memory

The Indonesian Stock Exchange (IDX) stock market index is one of the main indicators commonly used as a reference for national economic conditions. The value of the stock market index is often being used by investment companies and individual investors to help making investment decisions. Therefore, the ability to predict the stock market index value is a critical need. In the fields of statistics and probability theory as well as machine learning, various methods have been developed to predict the value of the stock market index with a good accuracy. However, previous research results have found that no one method is superior to other methods. This study proposes an ensemble model based on deep learning architecture, namely Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), called the CNN-LSTM. To be able to predict financial time series data, CNN-LSTM takes feature from CNN for extraction of important features from time series data, which are then integrated with LSTM feature that is reliable in processing time series data. Results of experiments on the proposed CNN-LSTM model confirm that the hybrid model effectively provides better predictive accuracy than the stand-alone time series data forecasting models, such as CNN and LSTM.  

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