scholarly journals A Two-Stage Household Electricity Demand Estimation Approach Based on Edge Deep Sparse Coding

Information ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 224 ◽  
Author(s):  
Yaoxian Liu ◽  
Yi Sun ◽  
Bin Li
Keyword(s):  
Sparse Coding ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Moving Average ◽  
Absolute Error ◽  
Demand Estimation ◽  
Electricity Demand ◽  
Accurate Estimation ◽  
Smart Meters ◽  
Two Stage

The widespread popularity of smart meters enables the collection of an immense amount of fine-grained data, thereby realizing a two-way information flow between the grid and the customer, along with personalized interaction services, such as precise demand response. These services basically rely on the accurate estimation of electricity demand, and the key challenge lies in the high volatility and uncertainty of load profiles and the tremendous communication pressure on the data link or computing center. This study proposed a novel two-stage approach for estimating household electricity demand based on edge deep sparse coding. In the first sparse coding stage, the status of electrical devices was introduced into the deep non-negative k-means-singular value decomposition (K-SVD) sparse algorithm to estimate the behavior of customers. The patterns extracted in the first stage were used to train the long short-term memory (LSTM) network and forecast household electricity demand in the subsequent 30 min. The developed method was implemented on the Python platform and tested on AMPds dataset. The proposed method outperformed the multi-layer perception (MLP) by 51.26%, the autoregressive integrated moving average model (ARIMA) by 36.62%, and LSTM with shallow K-SVD by 16.4% in terms of mean absolute percent error (MAPE). In the field of mean absolute error and root mean squared error, the improvement was 53.95% and 36.73% compared with MLP, 28.47% and 23.36% compared with ARIMA, 11.38% and 18.16% compared with LSTM with shallow K-SVD. The results of the experiments demonstrated that the proposed method can provide considerable and stable improvement in household electricity demand estimation.

scholarly journals COVID-19 Infection Forecasting based on Deep Learning in Iran

2020 ◽  
Author(s):  
Mehdi Azarafza ◽  
Mohammad Azarafza ◽  
Jafar Tanha
Keyword(s):  
Short Term Memory ◽  
Mean Squared Error ◽  
Moving Average ◽  
National Level ◽  
Absolute Error ◽  
Percentage Error ◽  
Autoregressive Integrated Moving Average ◽  
Error Metrics ◽  
Squared Error ◽  
Better Than

Since December 2019 coronavirus disease (COVID-19) is outbreak from China and infected more than 4,666,000 people and caused thousands of deaths. Unfortunately, the infection numbers and deaths are still increasing rapidly which has put the world on the catastrophic abyss edge. Application of artificial intelligence and spatiotemporal distribution techniques can play a key role to infection forecasting in national and province levels in many countries. As methodology, the presented study employs long short-term memory-based deep for time series forecasting, the confirmed cases in both national and province levels, in Iran. The data were collected from February 19, to March 22, 2020 in provincial level and from February 19, to May 13, 2020 in national level by nationally recognised sources. For justification, we use the recurrent neural network, seasonal autoregressive integrated moving average, Holt winter's exponential smoothing, and moving averages approaches. Furthermore, the mean absolute error, mean squared error, and mean absolute percentage error metrics are used as evaluation factors with associate the trend analysis. The results of our experiments show that the LSTM model is performed better than the other methods on the collected COVID-19 dataset in Iran

scholarly journals Prediction of COVID-19 Confirmed Cases after Vaccination: Based on Statistical and Deep Learning Models

2021 ◽  
Vol 3 (2) ◽  
pp. 153-165
Author(s):  
Meejoung Kim
Keyword(s):  
Deep Learning ◽  
Autocorrelation Function ◽  
Statistical Models ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Moving Average ◽  
Absolute Error ◽  
A Cell ◽  
Partial Autocorrelation Function ◽  
Autoregressive Conditional Heteroscedasticity

In this paper, we analyze and predict the number of daily confirmed cases of coronavirus (COVID-19) based on two statistical models and a deep learning (DL) model; the autoregressive integrated moving average (ARIMA), the generalized autoregressive conditional heteroscedasticity (GARCH), and the stacked long short-term memory deep neural network (LSTM DNN). We find the orders of the statistical models by the autocorrelation function and the partial autocorrelation function, and the hyperparameters of the DL model, such as the numbers of LSTM cells and blocks of a cell, by the exhaustive search. Ten datasets are used in the experiment; nine countries and the world datasets, from Dec. 31, 2019, to Feb. 22, 2021, provided by the WHO. We investigate the effects of data size and vaccination on performance. Numerical results show that performance depends on the used data's dates and vaccination. It also shows that the prediction by the LSTM DNN is better than those of the two statistical models. Based on the experimental results, the percentage improvements of LSTM DNN are up to 88.54% (86.63%) and 90.15% (87.74%) compared to ARIMA and GARCH, respectively, in mean absolute error (root mean squared error). While the performances of ARIMA and GARCH are varying according to the datasets. The obtained results may provide a criterion for the performance ranges and prediction accuracy of the COVID-19 daily confirmed cases.Doi: 10.28991/SciMedJ-2021-0302-7 Full Text: PDF

An efficient edge sparse coding approach to ultra-short-term household electricity demand estimation

2018 ◽  
Vol 13 (11) ◽  
pp. 1586-1594
Author(s):  
Yi Sun ◽  
Yaoxian Liu ◽  
Lu Zhang ◽  
Yongfeng Cao ◽  
Xiongwen Zhao
Keyword(s):  
Sparse Coding ◽  
Demand Estimation ◽  
Electricity Demand ◽  
Short Term

Fuzzy Crow Search Algorithm-Based Deep LSTM for Bitcoin Prediction

2020 ◽  
Vol 11 (4) ◽  
pp. 53-71
Author(s):  
Chandrasekar Ravi
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Short Term Memory ◽  
Search Algorithm ◽  
Moving Average ◽  
Absolute Error ◽  
Rate Of Change ◽  
Flow Strength ◽  
Optimal Weights ◽  
Double Exponential

Prediction of stock market trends is considered as an important task and is of great attention as predicting stock prices successfully may lead to attractive profits by making proper decisions. Stock market prediction is a major challenge owing to non-stationary, blaring, and chaotic data and thus, the prediction becomes challenging among the investors to invest the money for making profits. Initially, the blockchain network is fed to the blockchain network bridge from which the bitcoin data is acquired that is followed with the bitcoin prediction. Bitcoin prediction is performed using the proposed FuzzyCSA-based Deep Long short-term memory (LSTM). At first, the flow strength indicators are extracted based on Double exponential moving average (DEMA), Rate of Change (ROCR), Average True Range (ATR), Simple Moving Average (SMA), and Moving Average Convergence Divergence (MACD) from the blockchain data. Based on the extracted features, the prediction is done using FuzzyCSA-based Deep LSTM, which is the combination of FuzzyCSA with Deep LSTM. Then, the CSA is modified using the fuzzy operator for determining the optimal weights in Deep LSTM. The experimentation of the proposed method is performed from the openly available dataset. The analysis of the method in terms of Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) reveals that the proposed FuzzyCSA-based Deep LSTM acquired a minimal MAE of 0.4811, and the minimal RMSE of 0.3905, respectively.

scholarly journals ACCRUED FORECASTING ON TOURIST’S ARRIVAL IN BANGLADESH FOR SUSTAINABLE DEVELOPMENT

2021 ◽  
Vol 36 (2spl) ◽  
pp. 708-714
Author(s):  
Sayed Mohibul HOSSEN ◽  
◽  
Mohd Tahir ISMAIL ◽  
Mosab I. TABASH ◽  
Ahmed ABOUSAMAK ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Mean Squared Error ◽  
Moving Average ◽  
Critical Role ◽  
Absolute Error ◽  
Tourism Industry ◽  
Percentage Error ◽  
Absolute Deviation ◽  
Autoregressive Integrated Moving Average ◽  
Private And Public

Forecasting of potential tourists’ appearance could assume a critical role in the tourism industry, arranging at all levels in both the private and public sectors. In this study our aim to build an econometric model to forecast worldwide visitor streams to Bangladesh. For this purpose, the present investigation focuses on univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) modeling. Model choice criteria were Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Mean Squared Error (RMSE). As per descriptive statistics, the mean appearances were 207012 and will be 656522 (application) every year. Mean Absolute Deviation and Mean Squared Deviation likewise concurred with MAPE, MAE, and MSE. The result reveals that for sustainable development the SARIMA model is the reasonable model for forecasting universal visitor appearances in Bangladesh.

scholarly journals A study of univariate forecasting methods for crude oil price

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mei-Ling Cheng ◽  
Ching-Wu Chu ◽  
Hsiu-Li Hsu
Keyword(s):  
Regression Model ◽  
Crude Oil ◽  
Mean Squared Error ◽  
Moving Average ◽  
Model Performance ◽  
Absolute Error ◽  
Oil Price ◽  
Crude Oil Price ◽  
Content Type ◽  
Forecasting Methods

PurposeThis paper aims to compare different univariate forecasting methods to provide a more accurate short-term forecasting model on the crude oil price for rendering a reference to manages.Design/methodology/approachSix different univariate methods, namely the classical decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, the grey forecast, the hybrid grey model and the seasonal autoregressive integrated moving average (SARIMA), have been used.FindingsThe authors found that the grey forecast is a reliable forecasting method for crude oil prices.Originality/valueThe contribution of this research study is using a small size of data and comparing the forecasting results of the six univariate methods. Three commonly used evaluation criteria, mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percent error (MAPE), were adopted to evaluate the model performance. The outcome of this work can help predict the crude oil price.

scholarly journals RNN-based Dimensional Speech Emotion Recognition

2020 ◽  
Author(s):  
Bagus Tris Atmaja
Keyword(s):  
Emotion Recognition ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Recognition System ◽  
Speech Emotion Recognition ◽  
Percentage Error ◽  
Concordance Correlation ◽  
Acoustic Feature ◽  
Dense System

◆ A speech emotion recognition system based on recurrent neural networks is developed using long short-term memory networks.◆ Two of acoustic feature sets are evaluated: 31 Features (3 time-domain features, 5 frequency-domain features, 13 MFCCs, 5 F0s, and 5 Harmonics) and eGeMaps feature set (23 features).◆ To evaluate the performance, some metrics are used i.e. mean squared error (MSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and concordance correlation coefficient (CCC). Among those metrics, CCC is main focus as it is used by other researchers.◆ The developed system used multi-task learning to maximize arousal, valence, and dominance at the same time using CCC loss (1 - CCC). The result shows using LSTM networks improve the CCC score compared to baseline dense system. The best CCC score isobtained on arousal followed by dominance and valence.

scholarly journals Forecasting Inflation in Latin American Countries Using a SARIMA-LSTM Combination

2021 ◽  
Author(s):  
Rodrigo Peirano ◽  
Werner Kristjanpoller ◽  
Marcel Minutolo
Keyword(s):  
Latin American ◽  
Emerging Economies ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Moving Average ◽  
Inflation Forecasts ◽  
Inflation Forecasting ◽  
Forecasting Inflation ◽  
New Research ◽  
Model Confidence Set

Abstract Inflation forecasting has been and continues to be an important issue for the world's economies. Governments, through their central banks, watch closely inflation indicators to make national decisions and policies. Controlling growth and contraction requires governments to keep a close eye on the rate of inflation. When planning strategic national investments, governments attempt to forecast inflation over longer periods of time. Getting the inflation forecast wrong, can result in significant economic hardships. However, even given its significance, there is limited new research that applies updated methodologies to forecast it, and even fewer studies in emerging economies where inflation may be drastically higher. This study proposes to forecast the inflation rate in emerging economies based on the commonly used Seasonal Autoregressive Integrated Moving Average (SARIMA) approach combined with Long Short Term Memory (LSTM). The results indicate that the proposed model based on the combination of SARIMA and LSTM, have a higher accuracy in inflation forecasts as measured by the Mean Square Error (MSE) of the proposed models over the SARIMA model and LSTM alone. The loss function used is Mean Squared Error (MSE), and the Model Confidence Set (MCS) is used to test the superiority of the models in the economies of Mexico, Colombia and Peru.

scholarly journals Comparative Analysis of Different Univariate Forecasting Methods in Modelling and Predicting the Romanian Unemployment Rate for the Period 2021–2022

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 325
Author(s):  
Adriana AnaMaria Davidescu ◽  
Simona-Andreea Apostu ◽  
Andreea Paul
Keyword(s):  
Unemployment Rate ◽  
Mean Squared Error ◽  
Moving Average ◽  
Model Identification ◽  
Forecast Accuracy ◽  
Absolute Error ◽  
Forecast Horizon ◽  
Forecasting Performance ◽  
Sample Data ◽  
Out Of Sample

Unemployment has risen as the economy has shrunk. The coronavirus crisis has affected many sectors in Romania, some companies diminishing or even ceasing their activity. Making forecasts of the unemployment rate has a fundamental impact and importance on future social policy strategies. The aim of the paper is to comparatively analyze the forecast performances of different univariate time series methods with the purpose of providing future predictions of unemployment rate. In order to do that, several forecasting models (seasonal model autoregressive integrated moving average (SARIMA), self-exciting threshold autoregressive (SETAR), Holt–Winters, ETS (error, trend, seasonal), and NNAR (neural network autoregression)) have been applied, and their forecast performances have been evaluated on both the in-sample data covering the period January 2000–December 2017 used for the model identification and estimation and the out-of-sample data covering the last three years, 2018–2020. The forecast of unemployment rate relies on the next two years, 2021–2022. Based on the in-sample forecast assessment of different methods, the forecast measures root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percent error (MAPE) suggested that the multiplicative Holt–Winters model outperforms the other models. For the out-of-sample forecasting performance of models, RMSE and MAE values revealed that the NNAR model has better forecasting performance, while according to MAPE, the SARIMA model registers higher forecast accuracy. The empirical results of the Diebold–Mariano test at one forecast horizon for out-of-sample methods revealed differences in the forecasting performance between SARIMA and NNAR, of which the best model of modeling and forecasting unemployment rate was considered to be the NNAR model.

Predicting Coordinated Actuated Traffic Signal Change Times using Long Short-Term Memory Neural Networks

2021 ◽  
pp. 036119812110007
Author(s):  
Seifeldeen Eteifa ◽  
Hesham A. Rakha ◽  
Hoda Eldardiry
Keyword(s):  
Loss Function ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Loss Functions ◽  
Short Term ◽  
Mean Relative Error ◽  
Squared Error ◽  
The Mean ◽  
Long Short Term Memory

Vehicle acceleration and deceleration maneuvers at traffic signals result in significant fuel and energy consumption levels. Green light optimal speed advisory systems require reliable estimates of signal switching times to improve vehicle energy/fuel efficiency. Obtaining these estimates is difficult for actuated signals where the length of each green indication changes to accommodate varying traffic conditions and pedestrian requests. This study details a four-step long short-term memory (LSTM) deep learning based methodology that can be used to provide reasonable switching time estimates from green to red and vice versa while being robust to missing data. The four steps are data gathering, data preparation, machine learning model tuning, and model testing and evaluation. The input to the models includes controller logic, signal timing parameters, time of day, traffic state from detectors, vehicle actuation data, and pedestrian actuation data. The methodology is applied and evaluated on data from an intersection in Northern Virginia. A comparative analysis is conducted between different loss functions including the mean squared error, mean absolute error, and mean relative error used in LSTM and a new loss function that is proposed in this paper. The results show that while the proposed loss function outperforms conventional loss functions in overall absolute error values, the choice of the loss function is dependent on the prediction horizon. Specifically, the proposed loss function is slightly outperformed by the mean relative error for very short prediction horizons (less than 20 s) and the mean squared error for very long prediction horizons (greater than 120 s).

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