scholarly journals Forecasting PM2.5 Concentration Using a Single-Dense Layer BiLSTM Method

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1808
Author(s):  
Aji Teguh Prihatno ◽  
Himawan Nurcahyanto ◽  
Md. Faisal Ahmed ◽  
Md. Habibur Rahman ◽  
Md. Morshed Alam ◽  
...  
Keyword(s):  
Mean Square Error ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Percentage Error ◽  
Dense Layer ◽  
Mean Square ◽  
Time Data ◽  
Industrial Grade ◽  
Pm2.5 Concentration

In recent times, particulate matter (PM2.5) is one of the most critical air quality contaminants, and the rise of its concentration will intensify the hazard of cleanrooms. The forecasting of the concentration of PM2.5 has great importance to improve the safety of the highly pollutant-sensitive electronic circuits in the factories, especially inside semiconductor industries. In this paper, a Single-Dense Layer Bidirectional Long Short-term Memory (BiLSTM) model is developed to forecast the PM2.5 concentrations in the indoor environment by using the time series data. The real-time data samples of PM2.5 concentrations were obtained by using an industrial-grade sensor based on edge computing. The proposed model provided the best results comparing with the other existing models in terms of mean absolute error, mean square error, root mean square error, and mean absolute percentage error. These results show that the low error of forecasting PM2.5 concentration in a cleanroom in a semiconductor factory using the proposed Single-Dense Layer BiLSTM method is considerably high.

scholarly journals FORECASTING INTERNATIONAL TOURIST ARRIVALS IN MALAYSIA USING SARIMA AND HOLT-WINTERS MODEL

2020 ◽  
Vol 5 (18) ◽  
pp. 41-51
Author(s):  
Norliana Mohd Lip ◽  
Nur Shafiqah Jumery ◽  
Fatin Amira Ahmad Termizi ◽  
Nurul Atiqa Mulyadi ◽  
Norhasnelly Anuar ◽  
...  
Keyword(s):  
United Kingdom ◽  
Mean Square Error ◽  
Time Series Data ◽  
Tourism Industry ◽  
Series Data ◽  
Percentage Error ◽  
Mean Square ◽  
Public And Private ◽  
The United Kingdom ◽  
Tourism Flows

Tourism can be described as the activities of visitors who make a visit to the main destination outside their usual environment for less than a year for any purpose. The tourism industry has become one of the influential sectors in global economic growth. Thus, tourism forecasting plays an important role in public and private sectors concerning future tourism flows. This study is an attempt to determine the best model in forecasting the international tourist's arrival in Malaysia based on Box-Jenkins and Holt-Winters model. The comparison of the accuracy of the techniques between Box-Jenkins SARIMA and Holt-Winters model was done based on the value of Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The secondary time series data were obtained from the Tourism Malaysia Department, which consists of a number of tourist arrivals from Singapore, Korea, and the United Kingdom from the year 2013 until the year 2017. The findings of this study suggest that the SARIMA and Holt-Winters model are suitable to be used in forecasting tourist arrivals. This study found that the Holt-Winters model is the appropriate model to forecast tourist arrivals from the United Kingdom (UK) and Korea. While SARIMA (1,1,1) (1,1,1)12 is the appropriate model for forecasting tourist arrivals from Singapore.

scholarly journals STL-ATTLSTM: Vegetable Price Forecasting Using STL and Attention Mechanism-Based LSTM

Agriculture ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 612
Author(s):  
Helin Yin ◽  
Dong Jin ◽  
Yeong Hyeon Gu ◽  
Chang Jin Park ◽  
Sang Keun Han ◽  
...  
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Crop Production ◽  
Time Series Data ◽  
Short Term Memory ◽  
Attention Mechanism ◽  
Series Data ◽  
Percentage Error ◽  
Hot Pepper ◽  
Types Of Information

It is difficult to forecast vegetable prices because they are affected by numerous factors, such as weather and crop production, and the time-series data have strong non-linear and non-stationary characteristics. To address these issues, we propose the STL-ATTLSTM (STL-Attention-based LSTM) model, which integrates the seasonal trend decomposition using the Loess (STL) preprocessing method and attention mechanism based on long short-term memory (LSTM). The proposed STL-ATTLSTM forecasts monthly vegetable prices using various types of information, such as vegetable prices, weather information of the main production areas, and market trading volumes. The STL method decomposes time-series vegetable price data into trend, seasonality, and remainder components. It uses the remainder component by removing the trend and seasonality components. In the model training process, attention weights are assigned to all input variables; thus, the model’s prediction performance is improved by focusing on the variables that affect the prediction results. The proposed STL-ATTLSTM was applied to five crops, namely cabbage, radish, onion, hot pepper, and garlic, and its performance was compared to three benchmark models (i.e., LSTM, attention LSTM, and STL-LSTM). The performance results show that the LSTM model combined with the STL method (STL-LSTM) achieved a 12% higher prediction accuracy than the attention LSTM model that did not use the STL method and solved the prediction lag arising from high seasonality. The attention LSTM model improved the prediction accuracy by approximately 4% to 5% compared to the LSTM model. The STL-ATTLSTM model achieved the best performance, with an average root mean square error (RMSE) of 380, and an average mean absolute percentage error (MAPE) of 7%.

scholarly journals Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach

2020 ◽  
Vol 10 (20) ◽  
pp. 7079
Author(s):  
Elias Eze ◽  
Tahmina Ajmal
Keyword(s):  
Dissolved Oxygen ◽  
Mean Square Error ◽  
Short Term Memory ◽  
Sensor Data ◽  
Percentage Error ◽  
Forecasting Model ◽  
Mean Square ◽  
Intrinsic Mode Functions ◽  
Short Term ◽  
Do Concentration

Dissolved oxygen (DO) concentration is a vital parameter that indicates water quality. We present here DO short term forecasting using time series analysis on data collected from an aquaculture pond. This can provide the basis of data support for an early warning system, for an improved management of the aquaculture farm. The conventional forecasting approaches are commonly characterized by low accuracy and poor generalization problems. In this article, we present a novel hybrid DO concentration forecasting method with ensemble empirical mode decomposition (EEMD)-based LSTM (long short-term memory) neural network (NN). With this method, first, the sensor data integrity is improved through linear interpolation and moving average filtering methods of data preprocessing. Next, the EEMD algorithm is applied to decompose the original sensor data into multiple intrinsic mode functions (IMFs). Finally, the feature selection is used to carefully select IMFs that strongly correlate with the original sensor data, and integrate into both inputs for the NN. The hybrid EEMD-based LSTM forecasting model is then constructed. The performance of this proposed model in training and validation sets was compared with the observed real sensor data. To obtain the exact evaluation accuracy of the forecasted results of the hybrid EEMD-based LSTM forecasting model, four statistical performance indices were adopted: mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Results are presented for the short term (12-h) and the long term (1-month) that are encouraging, indicating suitability of this technique for forecasting DO values.

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).

Jaya Spider Monkey Optimization-driven Deep Convolutional LSTM for the prediction of COVID’19

2020 ◽  
Vol 16 (4) ◽  
Author(s):  
Satish Chander ◽  
Vijaya Padmanabha ◽  
Joseph Mani
Keyword(s):  
Time Series ◽  
Mean Square Error ◽  
Time Series Data ◽  
Moving Average ◽  
Spider Monkey ◽  
World Health ◽  
Series Data ◽  
Mean Square ◽  
The World ◽  
Spider Monkey Optimization

AbstractCOVID’19 is an emerging disease and the precise epidemiological profile does not exist in the world. Hence, the COVID’19 outbreak is treated as a Public Health Emergency of the International Concern by the World Health Organization (WHO). Hence, an effective and optimal prediction of COVID’19 mechanism, named Jaya Spider Monkey Optimization-based Deep Convolutional long short-term classifier (JayaSMO-based Deep ConvLSTM) is proposed in this research to predict the rate of confirmed, death, and recovered cases from the time series data. The proposed COVID’19 prediction method uses the COVID’19 data, which is the trending domain of research at the current era of fighting the COVID’19 attacks thereby, to reduce the death toll. However, the proposed JayaSMO algorithm is designed by integrating the Spider Monkey Optimization (SMO) with the Jaya algorithm, respectively. The Deep ConvLSTM classifier facilitates to predict the COVID’19 from the time series data based on the fitness function. Besides, the technical indicators, such as Relative Strength Index (RSI), Rate of Change (ROCR), Exponential Moving Average (EMA), Williams %R, Double Exponential Moving Average (DEMA), and Stochastic %K, are extracted effectively for further processing. Thus, the resulted output of the proposed JayaSMO-based Deep ConvLSTM is employed for COVID’19 prediction. Moreover, the developed model obtained the better performance using the metrics, like Mean Square Error (MSE), and Root Mean Square Error (RMSE) by considering confirmed, death, and the recovered cases of COVID’19 for China and Oman. Thus, the proposed JayaSMO-based Deep ConvLSTM showed improved results with a minimal MSE of 1.791, and the minimal RMSE of 1.338 based on confirmed cases in Oman. In addition, the developed model achieved the death cases with the values of 1.609, and 1.268 for MSE and RMSE, whereas the MSE and the RMSE value of 1.945, and 1.394 is achieved by the developed model using recovered cases in China.

scholarly journals A Survey of Forex and Stock Price Prediction Using Deep Learning

2021 ◽  
Vol 4 (1) ◽  
pp. 9 ◽  
Author(s):  
Zexin Hu ◽  
Yiqi Zhao ◽  
Matloob Khushi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Mean Square Error ◽  
Performance Metrics ◽  
Short Term Memory ◽  
Percentage Error ◽  
Mean Square ◽  
Learning Methods ◽  
Stock Price Prediction

Predictions of stock and foreign exchange (Forex) have always been a hot and profitable area of study. Deep learning applications have been proven to yield better accuracy and return in the field of financial prediction and forecasting. In this survey, we selected papers from the Digital Bibliography & Library Project (DBLP) database for comparison and analysis. We classified papers according to different deep learning methods, which included Convolutional neural network (CNN); Long Short-Term Memory (LSTM); Deep neural network (DNN); Recurrent Neural Network (RNN); Reinforcement Learning; and other deep learning methods such as Hybrid Attention Networks (HAN), self-paced learning mechanism (NLP), and Wavenet. Furthermore, this paper reviews the dataset, variable, model, and results of each article. The survey used presents the results through the most used performance metrics: Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Square Error (MSE), accuracy, Sharpe ratio, and return rate. We identified that recent models combining LSTM with other methods, for example, DNN, are widely researched. Reinforcement learning and other deep learning methods yielded great returns and performances. We conclude that, in recent years, the trend of using deep-learning-based methods for financial modeling is rising exponentially.

scholarly journals Prediksi Kedatangan Turis Asing ke Indonesia Menggunakan Backpropagation Neural Networks

2016 ◽  
Vol 4 (4) ◽  
pp. 485
Author(s):  
Haviluddin Haviluddin ◽  
Zainal Arifin ◽  
Awang Harsa Kridalaksana ◽  
Dedy Cahyadi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Mean Square Error ◽  
Time Series Data ◽  
Alternative Methods ◽  
Series Data ◽  
Mean Square ◽  
Backpropagation Neural Network ◽  
Forecasting Accuracy ◽  
The Mean

In this paper, a backpropagation neural network (BPNN) method with time series data have been explored. The BPNN method to predict the foreign tourist’s arrival to Indonesia datasets have been implemented. The foreign tourist’s arrival datasets were taken from the center agency on statistics (BPS) Indonesia. The experimental results showed that the BPNN method with two hidden layers were able to forecast foreign tourist’s arrival to Indonesia. Where, the mean square error (MSE) as forecasting accuracy has been indicated. In this study, the BPNN method is able and recommended to be alternative methods for predicting time series datasets. Also, the BPNN method showed that effective and easy to use. In other words, BPNN method is capable to producing good value of forecasting.Keywords - BPNN; foreign tourists; BPS; MSEPemanfaatan backpropagation neural network (BPNN) dengan data deret waktu telah digunakan dalam paper ini. Metode BPNN telah digunakan untuk memprediksi data kedatangan turis asing ke Indonesia, dimana data turis tersebut diambil dari badan pusat statistik Indonesia (BPS). Hasil pengujian menunjukkan bahwa metode BPNN dengan dua lapisan tersembunyi mampu memodelkan dan meramalkan data kedatangan turis asing ke Indonesia yang diindikasikan dengan nilai mean square error (MSE). Penelitian ini merekomendasikan bahwa metode BPNN mampu menjadi alternative metode dalam memprediksi data yang berjenis deret waktu karena metode BPNN efektif dan lebih mudah digunakan serta mampu menghasilkan akurasi nilai peramalan yang baik.

scholarly journals Forecasting Electricity Load With Hybrid Scalable Model Based on Stacked Non Linear Residual Approach

2021 ◽  
Vol 9 ◽  
Author(s):  
Ayush Sinha ◽  
Raghav Tayal ◽  
Aamod Vyas ◽  
Pankaj Pandey ◽  
O. P. Vyas
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Mean Square Error ◽  
Performance Metrics ◽  
Time Series Data ◽  
Hybrid Approach ◽  
Series Data ◽  
Mean Square ◽  
Power Load ◽  
Non Linear

Power has totally different attributes than other material commodities as electrical energy stockpiling is a costly phenomenon. Since it should be generated when demanded, it is necessary to forecast its demand accurately and efficiently. As electrical load data is represented through time series pattern having linear and non-linear characteristics, it needs a model that may handle this behavior well in advance. This paper presents a scalable and hybrid approach for forecasting the power load based on Vector Auto Regression (VAR) and hybrid deep learning techniques like Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN). CNN and LSTM models are well known for handling time series data. The VAR model separates the linear pattern in time series data, and CNN-LSTM is utilized to model non-linear patterns in data. CNN-LSTM works as CNN can extract complex features from electricity data, and LSTM can model temporal information in data. This approach can derive temporal and spatial features of electricity data. The experiment established that the proposed VAR-CNN-LSTM(VACL) hybrid approach forecasts better than more recent deep learning methods like Multilayer Perceptron (MLP), CNN, LSTM, MV-KWNN, MV-ANN, Hybrid CNN-LSTM and statistical techniques like VAR, and Auto Regressive Integrated Moving Average (ARIMAX). Performance metrics such as Mean Square Error, Root Mean Square Error, and Mean Absolute Error have been used to evaluate the performance of the discussed approaches. Finally, the efficacy of the proposed model is established through comparative studies with state-of-the-art models on Household Power Consumption Dataset (UCI machine learning repository) and Ontario Electricity Demand dataset (Canada).

Predicting Rice Production using Autoregressive Integrated Moving Average Model

2019 ◽  
Vol 6 (04) ◽  
Author(s):  
R C BHARATI ◽  
ANIL KUMAR SINGH
Keyword(s):  
Time Series ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Time Series Data ◽  
Moving Average ◽  
Information Criterion ◽  
Rice Production ◽  
Series Data ◽  
Mean Square ◽  
Autoregressive Integrated Moving Average

A study was conducted on time-series data on rice production in India. Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) time-series process was considered for predicting country's rice production using the time series data from 1950–51 to 2017–18. Data from 1950–51 to 2014–15 were used for model development and three years data from 2015–16 and 2017–18 were kept for validation The augmented Dicky Fuller test was applied to test stationarity in data set. Root mean square error. Based on ACF and PACF, the model was defined and tested for its suitability. Akaike information criterion and Bayesian information criterion were used to judge the suitability of the model to be fitted. The performance of the fitted model was examined using mean absolute error, mean percent forecast error, root mean square error and Theil's inequality coefficients. IMA (0, 1, 1) model performed well for forecasting purposes. The percent prediction error for the last three years i.e. from 2015–16 and 2017–18, was below 3%. The predicted values along with their standard errors up to the year 2099, were also obtained using the model.

scholarly journals Improved Prediction of COVID-19 Transmission and Mortality Using Google Search Trends for Symptoms in the United States

2021 ◽  
Author(s):  
Meshrif Alruily ◽  
Mohamed Ezz ◽  
Ayman Mohamed Mostafa ◽  
Nacim Yanes ◽  
Mostafa Abbas ◽  
...  
Keyword(s):  
Public Health ◽  
Time Series ◽  
Computational Models ◽  
Time Series Data ◽  
Short Term Memory ◽  
Emerging Infectious Diseases ◽  
The United States ◽  
Series Data ◽  
Percentage Error ◽  
Google Search

ABSTRACTAccurate forecasting of emerging infectious diseases can guide public health officials in making appropriate decisions related to the allocation of public health resources. Due to the exponential spread of the COVID-19 infection worldwide, several computational models for forecasting the transmission and mortality rates of COVID-19 have been proposed in the literature. To accelerate scientific and public health insights into the spread and impact of COVID-19, Google released the Google COVID-19 search trends symptoms open-access dataset. Our objective is to develop 7 and 14 -day-ahead forecasting models of COVID-19 transmission and mortality in the US using the Google search trends for COVID-19 related symptoms. Specifically, we propose a stacked long short-term memory (SLSTM) architecture for predicting COVID-19 confirmed and death cases using historical time series data combined with auxiliary time series data from the Google COVID-19 search trends symptoms dataset. Considering the SLSTM networks trained using historical data only as the base models, our base models for 7 and 14 -day-ahead forecasting of COVID cases had the mean absolute percentage error (MAPE) values of 6.6% and 8.8%, respectively. On the other side, our proposed models had improved MAPE values of 3.2% and 5.6%, respectively. For 7 and 14 -day-ahead forecasting of COVID-19 deaths, the MAPE values of the base models were 4.8% and 11.4%, while the improved MAPE values of our proposed models were 4.7% and 7.8%, respectively. We found that the Google search trends for “pneumonia,” “shortness of breath,” and “fever” are the most informative search trends for predicting COVID-19 transmission. We also found that the search trends for “hypoxia” and “fever” were the most informative trends for forecasting COVID-19 mortality.

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