scholarly journals Modeling digital camera monitoring count data with intermittent zeros for short-term prediction.

Heliyon ◽  
2022 ◽  
pp. e08774
Author(s):  
E. Afrifa-Yamoah ◽  
U.A. Mueller
Keyword(s):  
Count Data ◽  
Digital Camera ◽  
Short Term ◽  
Term Prediction ◽  
Short Term Prediction

Short–Term Prediction of Available Parking Space Based on Improved Wavelet Neural Network

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Jinfen Wang ◽  
Xiaofei Ye ◽  
Zhen Yang ◽  
Qiming Ye ◽  
Chang Yang
Keyword(s):  
Neural Network ◽  
Wavelet Neural Network ◽  
Parking Space ◽  
Short Term ◽  
Term Prediction ◽  
Short Term Prediction

Development of a Technique for Short-Term Prediction of Hydrometeors Using Advection and Physical Forcing.

1983 ◽  
Author(s):  
Gregory S. Forbes ◽  
John J. Cahir ◽  
Paul B. Dorian ◽  
Walter D. Lottes ◽  
Kathy Chapman
Keyword(s):  
Short Term ◽  
Physical Forcing ◽  
Term Prediction ◽  
Short Term Prediction

scholarly journals Novel robust time series analysis for long-term and short-term prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi
Keyword(s):  
Robust Regression ◽  
Simulated Data ◽  
New Method ◽  
Short Term ◽  
Least Absolute Deviations ◽  
Term Prediction ◽  
Regression Approach ◽  
Short Term Prediction ◽  
Long Term Prediction

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

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