scholarly journals Integrated Predictor Based on Decomposition Mechanism for PM2.5 Long-Term Prediction

2019 ◽  
Vol 9 (21) ◽  
pp. 4533 ◽  
Author(s):  
Xuebo Jin ◽  
Nianxiang Yang ◽  
Xiaoyi Wang ◽  
Yuting Bai ◽  
Tingli Su ◽  
...  
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Original Data ◽  
Multiple Components ◽  
Term Prediction ◽  
Gated Recurrent Units ◽  
Different Characteristics ◽  
Long Term Prediction ◽  
Beijing China

It is crucial to predict PM2.5 concentration for early warning regarding and the control of air pollution. However, accurate PM2.5 prediction has been challenging, especially in long-term prediction. PM2.5 monitoring data comprise a complex time series that contains multiple components with different characteristics; therefore, it is difficult to obtain an accurate prediction by a single model. In this study, an integrated predictor is proposed, in which the original data are decomposed into three components, that is, trend, period, and residual components, and then different sub-predictors including autoregressive integrated moving average (ARIMA) and two gated recurrent units are used to separately predict the different components. Finally, all the predictions from the sub-predictors are combined in fusion node to obtain the final prediction for the original data. The results of predicting the PM2.5 time series for Beijing, China showed that the proposed predictor can effectively improve prediction accuracy for long-term prediction.

Long-term prediction of karst water recovery process based on two different approaches in a former mining area, Transdanubian Range, Hungary

2020 ◽  
Author(s):  
Kamilla Modrovits ◽  
András Csepregi ◽  
József Kovács
Keyword(s):  
Time Series ◽  
Water Level ◽  
Trend Analysis ◽  
Recovery Process ◽  
Karst Water ◽  
Deterministic Approach ◽  
Term Prediction ◽  
Transdanubian Range ◽  
Long Term Prediction

<p>The Transdanubian Range is located in the mid-western part of Hungary and contains Mesozoic, mainly Triassic formations with the total thickness of 1.5-2 km. From 1950 to 1990 coal and bauxite mining took place with different centres in this area, therefor large amount of karst water was extracted for preventative purpose. Thus, the water levels decreased from ten to more than a hundred of meters. Since the mining was stopped in the beginning of the 1990s, the natural recharge exceeded the amount of extraction and the recovery of the karst water began. Since then the system is on the way to return to its original – undisturbed – state. Because of the rising water level, economic and technical engineering problems have occurred, which requires the better understanding of the process.</p><p>Water level changes are often predicted with a deterministic approach using different modelling software (e.g. MODFLOW, FEFLOW, etc.). However, stochastic approaches (e.g. trend estimation), which have so far been little used in forecast of groundwater, can also be applied for certain hydrogeological problems. The aims of the research were (i) to find the most accurate trend function describing the recovery process (ii) in order to make a long-term prediction, (iii) and compare the results with the results deterministic modelling. For this purpose, decades of time series from 107 monitoring wells were investigated.</p><p>As a result of the research, it was identified that the karst water time series from the Transdanubian Range can be properly estimated (R<sup>2</sup> > 0.9 in the 82.24% of the cases) by growth and logistic curves, especially by the so-called Richards and “63%” ones. These curves gave the best fit in 57.95% of the cases based on the R<sup>2</sup> value obtained by fitting the 10 examined models. Both the deterministic approach modelling (MODFLOW) and the stochastic approach trend analysis are suitable for estimating and predicting the water level rise in the karst aquifer, but the results are slightly different. Modelling with the MODFLOW software can be affected by the accuracy of input parameters (infiltration, yield of springs, etc.) and the realness of the conceptual model. First and foremost, more and better-quality water level data series are needed for trend analysis, and based on our prior knowledge, it is essential to provide an accurate expected maximum water level (upper limit). The comparison of the two methods unveiled, that growth and logistic curves can also be successfully used in the prediction of groundwater levels. As a conclusion, the number of methods which may be used for such research can be expanded.</p><p>This research is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 810980.</p>

Performance Similarity Based Method for Enhanced Prediction of Manufacturing Process Performance

2004 ◽  
Author(s):  
Jianbo Liu ◽  
Dragan Djurdjanovic ◽  
Jun Ni ◽  
Jay Lee
Keyword(s):  
Prediction Accuracy ◽  
Linear Prediction ◽  
Moving Average ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Term Prediction ◽  
Arma Modeling ◽  
Prediction Techniques ◽  
Long Term Prediction

Full realization of all potentials in predictive and proactive maintenance highly depends on the accuracy of long-term predictions of the remaining useful life of manufacturing equipment. Parametric linear prediction techniques, such as Autoregressive Moving Average modeling (ARMA), are routinely used to trend and predict future behavior of any time series, but are frequently not appropriate for long-term prediction because of the highly complicated and non-stationary nature of manufacturing processes. In this paper, we propose a novel method that is capable of achieving high long-term prediction accuracy by comparing signatures from two degradation processes using measures of similarity that form a Match Matrix. Through this concept, we can effectively include large amounts of historical information into the prediction of the current degradation process. Similarities with historical records are used to generate possible future distributions of features, which is then used to predict probabilities of failure over time by evaluating overlaps between predicted feature distributions and feature distributions related to unacceptable equipment behavior. Experimental results show that the proposed method results in a significant improvement of long-term prediction accuracy compared with ARMA modeling-based prediction.

scholarly journals Long-term prediction intervals of economic time series

2019 ◽  
Vol 58 (1) ◽  
pp. 191-222
Author(s):  
M. Chudý ◽  
S. Karmakar ◽  
W. B. Wu
Keyword(s):  
Time Series ◽  
Prediction Intervals ◽  
Economic Time Series ◽  
Term Prediction ◽  
Economic Time ◽  
Long Term Prediction

scholarly journals A Long-Term Prediction Model of Beijing Haze Episodes Using Time Series Analysis

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaoping Yang ◽  
Zhongxia Zhang ◽  
Zhongqiu Zhang ◽  
Liren Sun ◽  
Cui Xu ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Prediction Model ◽  
Industrial Development ◽  
Measurement Model ◽  
Vector Autoregressive ◽  
Series Analysis ◽  
Term Prediction ◽  
Long Term Prediction

The rapid industrial development has led to the intermittent outbreak of pm2.5 or haze in developing countries, which has brought about great environmental issues, especially in big cities such as Beijing and New Delhi. We investigated the factors and mechanisms of haze change and present a long-term prediction model of Beijing haze episodes using time series analysis. We construct a dynamic structural measurement model of daily haze increment and reduce the model to a vector autoregressive model. Typical case studies on 886 continuous days indicate that our model performs very well on next day’s Air Quality Index (AQI) prediction, and in severely polluted cases (AQI ≥ 300) the accuracy rate of AQI prediction even reaches up to 87.8%. The experiment of one-week prediction shows that our model has excellent sensitivity when a sudden haze burst or dissipation happens, which results in good long-term stability on the accuracy of the next 3–7 days’ AQI prediction.

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