scholarly journals Input Selection for Long-Term Prediction of Time Series

2005 ◽  
pp. 1002-1009 ◽  
Author(s):  
Jarkko Tikka ◽  
Jaakko Hollmén ◽  
Amaury Lendasse
Keyword(s):  
Time Series ◽  
Input Selection ◽  
Term Prediction ◽  
Selection For ◽  
Long Term Prediction

Sequential input selection algorithm for long-term prediction of time series

2008 ◽  
Vol 71 (13-15) ◽  
pp. 2604-2615 ◽  
Author(s):  
Jarkko Tikka ◽  
Jaakko Hollmén
Keyword(s):  
Time Series ◽  
Selection Algorithm ◽  
Input Selection ◽  
Term Prediction ◽  
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>

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.

Long-Term Prediction Intervals of Time Series

2010 ◽  
Vol 56 (3) ◽  
pp. 1436-1446 ◽  
Author(s):  
Zhou Zhou ◽  
Zhiwei Xu ◽  
Wei Biao Wu
Keyword(s):  
Time Series ◽  
Prediction Intervals ◽  
Term Prediction ◽  
Long Term Prediction
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