scholarly journals Temporal Analysis Of Temperature Variability In Pakistan Using The Method Of Empirical Mode Decomposition

2021 ◽  
Vol 10 (3) ◽  
pp. 1632-1638
Keyword(s):  
Neural Networks ◽  
Empirical Model ◽  
Significant Variation ◽  
Moving Average ◽  
Temporal Analysis ◽  
Average Temperature ◽  
Temperature And Precipitation ◽  
Mode Decomposition ◽  
Data Points ◽  
Auto Regressive

Pakistan is a country with altitudes that ranges from sea level to the world’s second-highest mountain peak in theworld. This distinctive characteristic renders a significant variation in the countries’ climate, for instance, temperature differences and spatial distribution of rainfall. These variations in the corresponding temperature and precipitation are best described and predicted by well-defined classifications of climatic variables and their corresponding spatial distributions across Pakistan. This paper demonstrates the techniques that allow us to predict the average temperature of Pakistan using empirical model decomposing with auto regressive integrated moving average (EMD-ARIMA) and empirical model decomposing with auto regressive integrated moving average with neural networks (EMD-ARIMA–NN) models. This research was applied to a lengthy series of average monthly temperature (report in oC) of Pakistan from January 1901 to December 2016 with 1392 data points. The precision of these models is checked with the avail of statistical analysis and error tests. Comparative analysis shows that the empirical model decomposing with auto regressive integrated moving average with neural networks has a more preponderant forecast

Modelling and control of greenhouse system using neural networks

2016 ◽  
Vol 40 (3) ◽  
pp. 918-929 ◽  
Author(s):  
A Manonmani ◽  
T Thyagarajan ◽  
M Elango ◽  
S Sutha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Moving Average ◽  
Growth Conditions ◽  
Operating Conditions ◽  
Adverse Weather ◽  
Control Schemes ◽  
Non Linear ◽  
Auto Regressive ◽  
And Control

A greenhouse system (GHS) is a closed structure that facilitates modified growth conditions to crops and provides protection from pests, diseases and adverse weather. However, a GHS exhibits non-linearity due to the interaction between the biological subsystem and the physical subsystem. Non-linear systems are difficult to control, particularly when their characteristics change with time. These systems are best handled with methods of computation intelligence, such as artificial neural networks (ANNs) and fuzzy systems. In the present work, the approximation capability of a neural network is used to model and control sufficient growth conditions of a GHS. An optimal neural network-based non-linear auto regressive with exogenous input (NARX) time series model is developed for a GHS. Based on the NARX model, two intelligent control schemes, namely a neural predictive controller (NPC) and non-linear auto regressive moving average (NARMA-L2) controller are proposed to achieve the desired growth conditions such as humidity and temperature for a better yield. Finally, closed-loop performances of the above two control schemes for servo and regulatory operations are analysed for various operating conditions using performance indices.

The Application of EMD and ARMA Bi-Cepstrum Fault Diagnosis Method in Gearbox of Overhead Traveling Crane

2011 ◽  
Vol 308-310 ◽  
pp. 88-91
Author(s):  
Hong Bo Xu ◽  
Guo Hua Chen ◽  
Xin Hua Wang ◽  
Jun Liang
Keyword(s):  
Fault Diagnosis ◽  
Evaluation Method ◽  
Moving Average ◽  
Arma Model ◽  
Time Varying ◽  
Stability Evaluation ◽  
Test Results ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Auto Regressive

For the time varying of signals, empirical mode decomposition (EMD) is occupied to modulate signals; auto-regressive moving average (ARMA) of higher accuracy is used to establish model for the signal principal components; then parametric bi-cepstrum estimation is implemented and fault feature is extracted. The test results about gearbox of overhead traveling crane indicate: the feature quefrency can be obtained through method of EMD and ARMA model parametric bi-cepstrum estimation.It is a kind of effective fault diagnosis and stability evaluation method.

scholarly journals Comparison of Stock Price Prediction Models using Pre-trained Neural Networks

2021 ◽  
Vol 3 (2) ◽  
pp. 122-134
Author(s):  
C Anand
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Stock Price ◽  
Linear Models ◽  
Moving Average ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Data Prediction ◽  
Comparison Results ◽  
Auto Regressive

Several intelligent data mining approaches, including neural networks, have been widely employed by academics during the last decade. In today's rapidly evolving economy, stock market data prediction and analysis play a significant role. Several non-linear models like neural network, generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive conditional heteroscedasticity (ARCH) as well as linear models like Auto-Regressive Integrated Moving Average (ARIMA), Moving Average (MA) and Auto Regressive (AR) may be used for stock forecasting. The deep learning architectures inclusive of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), Multilayer Perceptron (MLP) and Support Vector Machine (SVM) are used in this paper for stock price prediction of an organization by using the previously available stock prices. The National Stock Exchange (NSE) of India dataset is used for training the model with day-wise closing price. Data prediction is performed for a few sample companies selected on a random basis. Based on the comparison results, it is evident that the existing models are outperformed by CNN. The network can also perform stock predictions for other stock markets despite being trained with single market data as a common inner dynamics that has been shared between certain stock markets. When compared to the existing linear models, the neural network model outperforms them in a significant manner, which can be observed from the comparison results.

scholarly journals A Hybrid Forecasting Method for Solar Output Power Based on Variational Mode Decomposition, Deep Belief Networks and Auto-Regressive Moving Average

2018 ◽  
Vol 8 (10) ◽  
pp. 1901 ◽  
Author(s):  
Tuo Xie ◽  
Gang Zhang ◽  
Hongchi Liu ◽  
Fuchao Liu ◽  
Peidong Du
Keyword(s):  
Power Generation ◽  
Time Series ◽  
Empirical Mode Decomposition ◽  
Prediction Models ◽  
Moving Average ◽  
Variational Mode Decomposition ◽  
Mode Decomposition ◽  
Forecasting Method ◽  
Auto Regressive ◽  
Pv Power Generation

Due to the existing large-scale grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economical operation of electric power systems. In this study, a hybrid short-term forecasting method based on the Variational Mode Decomposition (VMD) technique, the Deep Belief Network (DBN) and the Auto-Regressive Moving Average Model (ARMA) is proposed to deal with the problem of forecasting accuracy. The DBN model combines a forward unsupervised greedy layer-by-layer training algorithm with a reverse Back-Projection (BP) fine-tuning algorithm, making full use of feature extraction advantages of the deep architecture and showing good performance in generalized predictive analysis. To better analyze the time series of historical data, VMD decomposes time series data into an ensemble of components with different frequencies; this improves the shortcomings of decomposition from Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) processes. Classification is achieved via the spectrum characteristics of modal components, the high-frequency Intrinsic Mode Functions (IMFs) components are predicted using the DBN, and the low-frequency IMFs components are predicted using the ARMA. Eventually, the forecasting result is generated by reconstructing the predicted component values. To demonstrate the effectiveness of the proposed method, it is tested based on the practical information of PV power generation data from a real case study in Yunnan. The proposed approach is compared, respectively, with the single prediction models and the decomposition-combined prediction models. The evaluation of the forecasting performance is carried out with the normalized absolute average error, normalized root-mean-square error and Hill inequality coefficient; the results are subsequently compared with real-world scenarios. The proposed approach outperforms the single prediction models and the combined forecasting methods, demonstrating its favorable accuracy and reliability.

scholarly journals Application of ARIMA Model for River Discharges Analysis

2017 ◽  
Vol 4 (1) ◽  
pp. 27 ◽  
Author(s):  
Bhola NS Ghimire
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Series Data ◽  
Hydrological Data ◽  
Time Component ◽  
Data Points ◽  
Auto Regressive ◽  
Component Time

<p class="Default">Time series data often arise when monitoring hydrological processes. Most of the hydrological data are time related and directly or indirectly their analysis related with time component. Time series analysis accounts for the fact that data points taken over time may have an internal structure (such as autocorrelation, trend or seasonal variation) that should be accounted for. Many methods and approaches for formulating time series forecasting models are available in literature. This study will give a brief overview of auto-regressive integrated moving average (ARIMA) process and its application to forecast the river discharges for a river. The developed ARIMA model is tested successfully for two hydrological stations for a river in US.</p><p><strong>Journal of Nepal Physical Society</strong><em><br /></em>Volume 4, Issue 1, February 2017, Page: 27-32</p>

scholarly journals Forecasting daily meteorological time series using ARIMA and regression models

2018 ◽  
Vol 32 (2) ◽  
pp. 253-264 ◽  
Author(s):  
Małgorzata Murat ◽  
Iwona Malinowska ◽  
Magdalena Gos ◽  
Jaromir Krzyszczak
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
R Software ◽  
Time Series Regression ◽  
Climatic Zones ◽  
Autoregressive Integrated Moving Average ◽  
Temperature And Precipitation ◽  
Auto Regressive

Abstract The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.

Prognosticating the spread of covid-19 pandemic based on optimal arima estimators

2020 ◽  
Vol 20 ◽  
Author(s):  
Venuka Sandhir ◽  
Vinod Kumar ◽  
Vikash Kumar
Keyword(s):  
South Africa ◽  
Statistical Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Stable Case ◽  
Johns Hopkins University ◽  
Modelling Techniques ◽  
Auto Regressive ◽  
Global Threat

Background: COVID-19 cases have been reported as a global threat and several studies are being conducted using various modelling techniques to evaluate patterns of disease dispersion in the upcoming weeks. Here we propose a simple statistical model that could be used to predict the epidemiological extent of community spread of COVID-19from the explicit data based on optimal ARIMA model estimators. Methods: Raw data was retrieved on confirmed cases of COVID-19 from Johns Hopkins University (https://github.com/CSSEGISandData/COVID-19) and Auto-Regressive Integrated Moving Average (ARIMA) model was fitted based on cumulative daily figures of confirmed cases aggregated globally for ten major countries to predict their incidence trend. Statistical analysis was completed by using R 3.5.3 software. Results: The optimal ARIMA model having the lowest Akaike information criterion (AIC) value for US (0,2,0); Spain (1,2,0); France (0,2,1); Germany (3,2,2); Iran (1,2,1); China (0,2,1); Russia (3,2,1); India (2,2,2); Australia (1,2,0) and South Africa (0,2,2) imparted the nowcasting of trends for the upcoming weeks. These parameters are (p, d, q) where p refers to number of autoregressive terms, d refers to number of times the series has to be differenced before it becomes stationary, and q refers to number of moving average terms. Results obtained from ARIMA model showed significant decrease cases in Australia; stable case for China and rising cases has been observed in other countries. Conclusion: This study tried their best at predicting the possible proliferate of COVID-19, although spreading significantly depends upon the various control and measurement policy taken by each country.

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