scholarly journals SMOOTHING IN NEURAL NETWORK FOR UNIVARIAT TIME SERIES DATA FORECASTING

2020 ◽  
Vol 3 (1) ◽  
pp. 23-30
Author(s):  
Nurfia Oktaviani Syamsiah ◽  
Indah Purwandani
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Exponential Smoothing ◽  
Series Data ◽  
Data Set ◽  
Research Material ◽  
Linear Relationships ◽  
The Neural Network ◽  
Optimal Accuracy

Time series data is interesting research material for many people. Not a few models have been produced, but very optimal accuracy has not been obtained. Neural network is one that is widely used because of its ability to understand non-linear relationships between data. This study will combine a neural network with exponential smoothing to produce higher accuracy. Exponential smoothing is one of the best linear methods is used for data set transformation and thereafter the new data set will be used in training and testing the Neural Network model. The resulting model will be evaluated using the standard error measure Root Mean Square Error (RMSE). Each model was compared with its RMSE value and then performed a T-Test. The proposed ES-NN model proved to have better predictive results than using only one method.

scholarly journals Remaining Useful Life Prediction Using Temporal Convolution with Attention

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 48-70
Author(s):  
Wei Ming Tan ◽  
T. Hui Teo
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Series Data ◽  
Multiple Time ◽  
Data Set ◽  
Form Complex ◽  
Useful Life

Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or a component. Such techniques often use sensor data which are periodically measured and recorded into a time series data set. Such multivariate data sets form complex and non-linear inter-dependencies through recorded time steps and between sensors. Many current existing algorithms for prognostic purposes starts to explore Deep Neural Network (DNN) and its effectiveness in the field. Although Deep Learning (DL) techniques outperform the traditional prognostic algorithms, the networks are generally complex to deploy or train. This paper proposes a Multi-variable Time Series (MTS) focused approach to prognostics that implements a lightweight Convolutional Neural Network (CNN) with attention mechanism. The convolution filters work to extract the abstract temporal patterns from the multiple time series, while the attention mechanisms review the information across the time axis and select the relevant information. The results suggest that the proposed method not only produces a superior accuracy of RUL estimation but it also trains many folds faster than the reported works. The superiority of deploying the network is also demonstrated on a lightweight hardware platform by not just being much compact, but also more efficient for the resource restricted environment.

International Real Estate Review

2004 ◽  
Vol 7 (1) ◽  
pp. 121-138
Author(s):  
Xin J. Ge ◽  
◽  
G. Runeson ◽  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hong Kong ◽  
Time Series Data ◽  
Series Data ◽  
Residential Property ◽  
Neural Network Approach ◽  
Price Performance ◽  
The Neural Network ◽  
Property Price

This paper develops a forecasting model of residential property prices for Hong Kong using an artificial neural network approach. Quarterly time-series data are applied for testing and the empirical results suggest that property price index, lagged one period, rental index, and the number of agreements for sales and purchases of units are the major determinants of the residential property price performance in Hong Kong. The results also suggest that the neural network methodology has the ability to learn, generalize, and converge time series.

Variance error of multi-classification based anomaly detection for time series data

2020 ◽  
pp. 1-16
Author(s):  
Baoquan Wang ◽  
Tonghai Jiang ◽  
Xi Zhou ◽  
Bo Ma ◽  
Fan Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Short Term Memory ◽  
Computational Cost ◽  
Reconstruction Error ◽  
Detection Methods ◽  
Series Data ◽  
Data Set

For abnormal detection of time series data, the supervised anomaly detection methods require labeled data. While the range of outlier factors used by the existing semi-supervised methods varies with data, model and time, the threshold for determining abnormality is difficult to obtain, in addition, the computational cost of the way to calculate outlier factors from other data points in the data set is also very large. These make such methods difficult to practically apply. This paper proposes a framework named LSTM-VE which uses clustering combined with visualization method to roughly label normal data, and then uses the normal data to train long short-term memory (LSTM) neural network for semi-supervised anomaly detection. The variance error (VE) of the normal data category classification probability sequence is used as outlier factor. The framework enables anomaly detection based on deep learning to be practically applied and using VE avoids the shortcomings of existing outlier factors and gains a better performance. In addition, the framework is easy to expand because the LSTM neural network can be replaced with other classification models. Experiments on the labeled and real unlabeled data sets prove that the framework is better than replicator neural networks with reconstruction error (RNN-RS) and has good scalability as well as practicability.

scholarly journals Toward an embedded system for gesture recognition based on artificial neural network using reconfigurable target (case study and review)

2020 ◽  
Vol 10 (4) ◽  
pp. 142-148
Author(s):  
Ahmad Reda ◽  
Tareq Alshoufi ◽  
Ahmed Bouzid ◽  
József Vásárhelyi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Embedded System ◽  
Time Series Data ◽  
Reference Signal ◽  
Series Data ◽  
The Neural Network ◽  
Human Arm ◽  
On Chip

With a view to create an intelligent remote control for robot movements, this article treats the study case of dataset creation using RSG (Reference Signal Generator). Using artificial intelligence, the device recognizes the gestures of an operator. Indeed, a neural network can classify time series data coming from accelerometers, and for a beginning 4 gestures are taken into consideration. The most challenging work is to build a reference dataset that is necessary for the learning process. To train the neural network, a huge amount of reference data should be created (hundreds of thousands of time-series vectors per gesture per sensor), which cannot be done manually by an operator. To overcome the issue, an RSG is created. This article also describes how a 1-DoF arm has been designed to emulate the behavior of the human arm doing gestures as well as the data acquisition system. The system is based on a software/hardware co-design implemented on Programmable System on Chip (PSoC).

scholarly journals Combining PI Sigma Neural Network with Multiple Offspring Genetic Algorithm for Stock Market Price Prediction

2019 ◽  
Vol 9 (1) ◽  
pp. 6934-6939
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Time Series ◽  
Stock Market ◽  
Time Series Data ◽  
Market Price ◽  
Series Data ◽  
Average Error ◽  
Data Set ◽  
Proposed Model

Accurate and precise prediction of pricing of stock market is a very demanding task because of volatile, chaotic nature of time series data. Artificial Neural Networks played a major role for solving diversified problems for its robustness, strong capability for solving non linear problems and generalization ability. It is a popular choice for researchers for foretelling the financial time series data. In the article Pi Sigma Neural Network (PSNN) is developed for foretelling of stock market pricing in different time horizons. Pricing of stock market is predicted for one, fifteen and thirty days in advance. The parameters of the network are interpreted and optimized by Multiple Offspring Genetic Algorithm (MOGA). The motivation of this study is to achieve global optima with faster convergence rate. Bombay stock Exchange (BSE) data set is used for implementing the proposed model. Performance of the proposed model is evaluated using metrics like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Median Average Error (MedAE) . The results are compared with Pi Sigma Neural Network with Genetic Algorithm (PSNN-GA) and Pi Sigma Neural Network with Differential Evolution (PSNN-DE). It is concluded that the proposed model outperforms PSNN-GA and PSNN-DE models

Artificial neural network forecasting of nonlinear Markov processes

2007 ◽  
Vol 85 (3) ◽  
pp. 279-294 ◽  
Author(s):  
Sean W Fleming
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
River Flow ◽  
Time Series Data ◽  
Series Data ◽  
Underlying Structure ◽  
Good Prediction ◽  
Data Set ◽  
Artificial Neural

I assessed the performance characteristics of the feed-forward artificial neural network (ANN) as a first-order nonlinear Markov modelling technique. The ability to recover the underlying structure of five synthetic random time series was first tested. The method was then applied to an observed geophysical time series, and the results were compared against external empirical constraints and a simple representation of the underlying physics. The Monte Carlo experiments suggested that the ANN–Markov technique: (i) yields good prediction skill; (ii) in general, accurately retrieves the form of the iterative mapping, even for extremely noisy data; (iii) accomplishes the foregoing without any need to consider or adjust for the distributional characteristics of the data or driving noise; and (iv) accurately estimates the distribution of the strictly stochastic signal component. Application to a historical river-flow record again showed good forecast skill. Moreover, the robustness, flexibility, and simplicity of the method permitted easy identification of the fundamental nonlinear physical dynamics of this environmental system directly from the time series data, perhaps belying the common perception of ANNs as a strictly black-box prediction technique. The ANN–Markov technique may thus serve as a valuable data-driven tool for guiding the development of both process-based and parameteric statistical models. The lack of specific distributional assumptions and requirements notwithstanding, it was also found that manual distributional transformations may permit the method to be tuned to particular applications by emphasizing or de-emphasizing certain features of the data. Drawbacks to the method include substantial data-set length requirements, a general limitation of ANNs, as well as an inconsistent but potentially troubling tendency to partially imprint the form of the ANN activation function upon the estimated recursion relationship. PACS Nos.: 02.50.Ga, 05.10.–a, 05.45.Tp, 07.05.Mh, 02.50.Ey, 92.40.Fb

scholarly journals Forecasting Number of COVID-19 in Bali Province Using Neural Network Algorithm

2021 ◽  
pp. 72
Author(s):  
Ida Ayu Utari Dewi ◽  
I Kadek Noppi Adi Jaya ◽  
Kadek Oky Sanjaya
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Large Family ◽  
Series Data ◽  
Severe Illness ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
The Common

COVID-19 (coronavirus disease 2019) is a large family of viruses that cause mild to severe illness, such as the common cold or colds and serious illnesses such as MERS and SARS. COVID-19 has become a pandemic, meaning that there has been an increase in cases of the disease which is quite fast and there has been spread between countries and caused enormous losses in various countries. The increasing number of COVID-19 cases every day in Indonesia, including in Bali Province and the resulting losses underlie the forecasting of the number of COVID-19 in Bali Province. Forecasting is carried out using the Neural Network algorithm for time series data on the number of COVID-19 in Bali Province. The data used is data on the number of COVID-19 in the Bali Province in the form of time series data sourced from the Bali Provincial Health Office. The entire forecasting process uses the Rapidminer Studio tools starting from preprocessing, modeling, testing and validation. The results of the RMSE (Root Mean Square Error) evaluation value based on testing for the positive patients were 18.956, the patients recovered were 15.413, the patients under treatment were 5.066 and the patients who died was 0.233.

Neural Network Techniques for Time Series Prediction: A Review

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Series Data ◽  
Prediction Problem ◽  
Neural Network Models ◽  
Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

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