The Development of a Hybrid Wavelet-ARIMA-LSTM Model for Precipitation Amounts and Drought Analysis

Investigation of quantitative predictions of precipitation amounts and forecasts of drought events are conducive to facilitating early drought warnings. However, there has been limited research into or modern statistical analyses of precipitation and drought over Northeast China, one of the most important grain production regions. Therefore, a case study at three meteorological sites which represent three different climate types was explored, and we used time series analysis of monthly precipitation and the grey theory methods for annual precipitation during 1967–2017. Wavelet transformation (WT), autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) methods were utilized to depict the time series, and a new hybrid model wavelet-ARIMA-LSTM (W-AL) of monthly precipitation time series was developed. In addition, GM (1, 1) and DGM (1, 1) of the China Z-Index (CZI) based on annual precipitation were introduced to forecast drought events, because grey system theory specializes in a small sample and results in poor information. The results revealed that (1) W-AL exhibited higher prediction accuracy in monthly precipitation forecasting than ARIMA and LSTM; (2) CZI values calculated through annual precipitation suggested that more slight drought events occurred in Changchun while moderate drought occurred more frequently in Linjiang and Qian Gorlos; (3) GM (1, 1) performed better than DGM (1, 1) in drought event forecasting.

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Time Series Adaptive Online Prediction Method Combined with Modified LS-SVR and AGO

Mathematical Problems in Engineering ◽

10.1155/2012/985930 ◽

2012 ◽

Vol 2012 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Guo Yangming ◽

Zhang Lu ◽

Cai Xiaobin ◽

Ran Congbao ◽

Zhai Zhengjun ◽

...

Keyword(s):

Time Series ◽

Complex Systems ◽

Incremental Learning ◽

Learning Algorithm ◽

Computing Time ◽

Grey System Theory ◽

Good Prediction ◽

Grey System ◽

Online Prediction ◽

Prediction Approach

Fault or health condition prediction of the complex systems has attracted more attention in recent years. The complex systems often show complex dynamic behavior and uncertainty, which makes it difficult to establish a precise physical model. Therefore, the time series of complex system is used to implement prediction in practice. Aiming at time series online prediction, we propose a new method to improve the prediction accuracy in this paper, which is based on the grey system theory and incremental learning algorithm. In this method, the accumulated generating operation (AGO) with the raw time series is taken to improve the data quality and regularity firstly; then the prediction is conducted by a modified LS-SVR model, which simplifies the calculation process with incremental learning; finally, the inverse accumulated generating operation (IAGO) is performed to get the prediction results. The results of the prediction experiments indicate preliminarily that the proposed scheme is an effective prediction approach for its good prediction precision and less computing time. The method will be useful in actual application.

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Analysis and Prediction of Influencing Parameters on the Coal Classification Performance of a Novel Three Products Hydrocyclone Screen (TPHS) Based on Grey System Theory

Processes ◽

10.3390/pr8080974 ◽

2020 ◽

Vol 8 (8) ◽

pp. 974

Author(s):

Chuanzhen Wang ◽

Xiaolu Sun ◽

Liang Shen ◽

Guanghui Wang

Keyword(s):

Performance Evaluation ◽

Effective Means ◽

Grey System Theory ◽

Small Samples ◽

Grey Theory ◽

Aperture Size ◽

Grey System ◽

Feed Concentration ◽

Feed Pressure ◽

Vortex Finder

A novel hydrocyclone including a cylindrical screen embedded in a conventional hydrocyclone (CH), named three products hydrocyclone screen (TPHS), has been successfully designed. In TPHS, the combination of centrifugal classification and screening was employed to separate particles. In this paper, Grey theory, as an effective means to the laws of both complex and uncertainty system’s behavior with small samples, was used to investigate the operational (feed concentration and feed pressure) and structural (aperture size, spigot diameter, and vortex finder diameter) parameters on performance evaluation Hancock classification efficiency (HE), imperfection (I), and cut size (d50c). The experiments of coal sample (0–1 mm) show that TPHS with coarser particles in underflow exhibited the absent “fish-hook”. The closeness calculated using the Grey System algorithm indicates that the performance of TPHS was closely related to the operation and structure parameters. Further, the order of grey incidence degree between different parameters and HE (or I or d50c) is the spigot diameter and aperture size with the highest value, the feed pressure and vortex finder diameter with the middle value, and the feed concentration with the lowest value. The prediction using the GM(1, N) algorithm implies that the dynamic prediction model for the performance evaluation can be created depending on the operation, structure and previous performance value. The mean relative errors between the predicted and actual HE, I, and d50 were 2.84%, 5.83%, and 3.57%, respectively, which exhibit the accurate prediction.

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Assessment of airport performance using the grey theory method

Grey Systems Theory and Application ◽

10.1108/gs-07-2017-0023 ◽

2017 ◽

Vol 7 (3) ◽

pp. 426-436 ◽

Cited By ~ 6

Author(s):

Mohamed Ibrahim Eshtaiwi ◽

Ibrahim A. Badi ◽

Ali M. Abdulshahed ◽

Turan Erman Erkan

Keyword(s):

System Theory ◽

Performance Evaluation ◽

Service Quality ◽

Design Methodology ◽

Grey System Theory ◽

Grey Theory ◽

Theory Method ◽

Grey System ◽

Content Type ◽

Overall Performance

Purpose Performance evaluation of airports or any other organisation is paramount for improving performance. The purpose of this paper is to evaluate and compare the performance of the three international airports in Libya (MJI, MRA, and LAQ airports) by considering five aspects of performance. Design/methodology/approach The considered aspects are airport service quality, airport operations, airport economy, safety and security, and environmental. The paper uses the grey system theory to assess these airports by summarizing the opinions of experts. Findings The finding of this study provides directions of the evaluated airports to take the correct actions to improve overall performance. Originality/value No literature has been found till date is to evaluate and compare the performance of the international airports in Libya.

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Application of Grey System Theory in the Dynamic Monitoring of Bridge

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.501-504.829 ◽

2014 ◽

Vol 501-504 ◽

pp. 829-833

Author(s):

Cheng Xin Yu ◽

Zheng Wen Yu ◽

Yong Qian Zhao ◽

Jia Dong Zhang

Keyword(s):

Dynamic Deformation ◽

Active Role ◽

Theory Model ◽

Grey System Theory ◽

Dynamic Monitoring ◽

Grey Theory ◽

Observation Error ◽

Bridge Structure ◽

Grey System ◽

Safety And Reliability

It has great significance to ensure the safety of bridge structure by using information technology to monitor the bridge dynamic deformation and find out problems in time, besides, it is more effective to make analysis of the bridge by using grey theory model. The combination of the two methods could avoid the observation error, and play an active role in improving the safety and reliability of bridge.

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Grey system based novel approach for stock market forecasting

Grey Systems Theory and Application ◽

10.1108/gs-04-2015-0014 ◽

2015 ◽

Vol 5 (2) ◽

pp. 178-193 ◽

Cited By ~ 10

Author(s):

R.M. Kapila Tharanga Rathnayaka ◽

D.M.K.N Seneviratna ◽

Wei Jianguo

Keyword(s):

Time Series ◽

Prediction Models ◽

Stock Exchange ◽

Moving Average ◽

Grey Prediction ◽

Grey System ◽

Short Term ◽

Content Type ◽

Stock Market Forecasting ◽

Novel Approach

Purpose – Making decisions in finance have been regarded as one of the biggest challenges in the modern economy today; especially, analysing and forecasting unstable data patterns with limited sample observations under the numerous economic policies and reforms. The purpose of this paper is to propose suitable forecasting approach based on grey methods in short-term predictions. Design/methodology/approach – High volatile fluctuations with instability patterns are the common phenomenon in the Colombo Stock Exchange (CSE), Sri Lanka. As a subset of the literature, very few studies have been focused to find the short-term forecastings in CSE. So, the current study mainly attempted to understand the trends and suitable forecasting model in order to predict the future behaviours in CSE during the period from October 2014 to March 2015. As a result of non-stationary behavioural patterns over the period of time, the grey operational models namely GM(1,1), GM(2,1), grey Verhulst and non-linear grey Bernoulli model were used as a comparison purpose. Findings – The results disclosed that, grey prediction models generate smaller forecasting errors than traditional time series approach for limited data forecastings. Practical implications – Finally, the authors strongly believed that, it could be better to use the improved grey hybrid methodology algorithms in real world model approaches. Originality/value – However, for the large sample of data forecasting under the normality assumptions, the traditional time series methodologies are more suitable than grey methodologies; especially GM(1,1) give some dramatically unsuccessful results than auto regressive intergrated moving average in model pre-post stage.

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A REPAIRABLE SYSTEM MODELLING: COMBINING GREY SYSTEM THEORY WITH INTERVAL-VALUED FUZZY SET THEORY

International Journal of Reliability Quality and Safety Engineering ◽

10.1142/s0218539305001811 ◽

2005 ◽

Vol 12 (03) ◽

pp. 241-266 ◽

Cited By ~ 7

Author(s):

R. GUO

Keyword(s):

System Theory ◽

Fuzzy Sets ◽

Underlying Mechanism ◽

Grey System Theory ◽

Small Sample ◽

System Modelling ◽

Repairable System ◽

Type I ◽

Grey System ◽

Interval Valued

A fundamental but impossible to be addressed problem in repairable system modelling is how to estimate the system repair improvement (or damage) effects because of the large-sample requirements from the standard statistical inference theory. On the other hand, repairable system operating and maintenance data are often imprecise and vague and therefore Type I fuzzy sets defined by point-wise membership functions are often used for the modelling repairable systems. However, it is more logical and natural to argue that Type II fuzzy sets defined by interval-valued membership function, called interval-valued fuzzy sets (IVFS), should be used in characterizing the underlying mechanism of repairable system. In this paper, we explore a small-sample based GM(1,1) modelling approach rooted in the grey system theory to extract the system intrinsic functioning times from the seemly lawless functioning-failure time records and thus to estimate the repair improvement (damage) effects. We further explore the role of interval-valued fuzzy sets theory in the analysis of the system underlying mechanism. We develop a framework of the GM(1,1)-IVFS mixed reliability analysis and illustrate our idea by an industrial example.

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Analysis and Models for the Friction Coefficient of a Sliding Bearing

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.395-396.826 ◽

2013 ◽

Vol 395-396 ◽

pp. 826-830

Author(s):

Bao Ming Wang ◽

Jin Xin Xu ◽

Fei Zhu ◽

Zai Xin Wu

Keyword(s):

Friction Coefficient ◽

Rotational Speed ◽

Moving Average ◽

Grey System Theory ◽

Grey System ◽

Sliding Bearings ◽

Sliding Bearing ◽

Non Linear ◽

Grey Relational Grade ◽

Grey Relational

In this paper, the analyzing and modeling of the friction coefficient in sliding bearing is reported. Based on the grey system theory, the effects of rotational speed and load on the friction coefficient of the sliding bearings are investigated. The grey relational grade is an important parameter to measure the effects of rotational speed and load on friction coefficient of the sliding bearings. The grey relational grade analysis shows that load has an even more significant effect upon the friction coefficient compared with rotational speed. On the basis of analyzing and processing the experimental data, a nonlinear model for friction coefficient of a sliding bearing have been set up by NARMAX Non-Linear Auto-Regressive Moving Average with Exogenous Input. It was found that the NARMAX Non-Linear model has good accuracy and is applicable for the calculation of friction coefficient in sliding bearing.

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Minimum Message Length in Hybrid ARMA and LSTM Model Forecasting

10.20944/preprints202110.0049.v1 ◽

2021 ◽

Author(s):

Zheng Fang ◽

David L. Dowe ◽

Shelton Peiris ◽

Dedi Rosadi

Keyword(s):

Time Series ◽

Deep Learning ◽

Short Term Memory ◽

Moving Average ◽

Arma Model ◽

Autoregressive Moving Average ◽

Information Theoretic ◽

Minimum Message Length ◽

Message Length ◽

Long Short Term Memory

We investigate the power of time series analysis based on a variety of information-theoretic approaches from statistics (AIC, BIC) and machine learning (Minimum Message Length) - and we then compare their efficacy with traditional time series model and with hybrids involving deep learning. More specifically, we develop AIC, BIC and Minimum Message Length (MML) ARMA (autoregressive moving average) time series models - with this Bayesian information-theoretic MML ARMA modelling already being new work. We then study deep learning based algorithms in time series forecasting, using Long Short Term Memory (LSTM), and we then combine this with the ARMA modelling to produce a hybrid ARMA-LSTM prediction. Part of the purpose of the use of LSTM is to seek capture any hidden information in the residuals left from the traditional ARMA model. We show that MML not only outperforms earlier statistical approaches to ARMA modelling, but we further show that the hybrid MML ARMA-LSTM models outperform both ARMA models and LSTM models.

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Spatio-Temporal Trends of Monthly and Annual Precipitation in Aguascalientes, Mexico

Atmosphere ◽

10.3390/atmos11050437 ◽

2020 ◽

Vol 11 (5) ◽

pp. 437

Author(s):

Osías Ruiz-Alvarez ◽

Vijay P. Singh ◽

Juan Enciso-Medina ◽

Ronald Ernesto Ontiveros-Capurata ◽

Arturo Corrales-Suastegui

Keyword(s):

Time Series ◽

Annual Precipitation ◽

Irrigated Agriculture ◽

Monthly Precipitation ◽

Precipitation Trend ◽

Precipitation Time Series ◽

June July August ◽

Precipitation Decrease ◽

Weather Stations ◽

June July

The objective of this research was to analyze the temporal patterns of monthly and annual precipitation at 36 weather stations of Aguascalientes, Mexico. The precipitation trend was determined by the Mann–Kendall method and the rate of change with the Theil–Sen estimator. In total, 468 time series were analyzed, 432 out of them were monthly, and 36 were annual. Out of the total monthly precipitation time series, 42 series showed a statistically significant trend (p ≤ 0.05), from which 8/34 showed a statistically significant negative/positive trend. The statistically significant negative trends of monthly precipitation occurred in January, April, October, and December. These trends denoted more significant irrigation water use, higher water extractions from the aquifers in autumn–winter, more significant drought occurrence, low forest productivity, higher wildfire risk, and greater frost risk. The statistically significant positive trends occurred in May, June, July, August, and September; to a certain extent, these would contribute to the hydrology, agriculture, and ecosystem but also could provoke problems due to water excess. In some months, the annual precipitation variability and El Niño-Southern Oscillation (ENSO) were statistically correlated, so it could be established that in Aguascalientes, this phenomenon is one of the causes of the yearly precipitation variation. Out of the total annual precipitation time series, only nine series were statistically significant positive; eight out of them originated by the augments of monthly precipitation. Thirteen weather stations showed statistically significant trends in the total precipitation of the growing season (May, June, July, August, and September); these stations are located in regions of irrigated agriculture. The precipitation decrease in dry months can be mitigated using shorter cycle varieties with lower water consumption, irrigation methods with high efficiency, and repairing irrigation infrastructure. The precipitation increase in humid months can be used to store water and use it during the dry season, and its adverse effects can be palliated with the use of varieties resistant to root diseases and lodging. The results of this work will be beneficial in the management of agriculture, hydrology, and water resources of Aguascalientes and in neighboring arid regions affected by climate change.

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