Crop maturity in relation to quality of canned cream style sweet corn

1951 ◽  
Vol 2 (1) ◽  
pp. 43
Author(s):  
RS Mitchell ◽  
LJ Lynch
Keyword(s):  
Sweet Corn ◽  
Moisture Loss ◽  
Term Prediction ◽  
Grain Moisture ◽  
Heat Increment ◽  
Moisture Percentage ◽  
Short Term Prediction ◽  
Long Term Prediction

The reliability of grain moisture percentage as a measure of desirable picking maturity was confirmed. The refractive index of the fluid material expressed from the grain was shown to give a rapid estimation of the grain moisture, the accuracy being sufficient for commercial requirements. The rate of moisture loss from ripening grain grown at Windsor, N.S.W., was measured and used for the short-term prediction of the date of harvest. Limits were defined for different planting times in terms of the number of days to picking maturity. Such limits provide a simpler means of long-term prediction than the heat increment method proposed by American workers. A plan is outlined for the field control of commercial sweet corn plantings in the Windsor district.

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.

scholarly journals Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs Models

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Wei Ming ◽  
Yukun Bao ◽  
Zhongyi Hu ◽  
Tao Xiong
Keyword(s):  
Prediction Models ◽  
Nonlinear Modeling ◽  
Real Data ◽  
Data Preprocessing ◽  
Air Transportation ◽  
Data Sets ◽  
Term Prediction ◽  
Short Term Prediction ◽  
Long Term Prediction

The hybrid ARIMA-SVMs prediction models have been established recently, which take advantage of the unique strength of ARIMA and SVMs models in linear and nonlinear modeling, respectively. Built upon this hybrid ARIMA-SVMs models alike, this study goes further to extend them into the case of multistep-ahead prediction for air passengers traffic with the two most commonly used multistep-ahead prediction strategies, that is, iterated strategy and direct strategy. Additionally, the effectiveness of data preprocessing approaches, such as deseasonalization and detrending, is investigated and proofed along with the two strategies. Real data sets including four selected airlines’ monthly series were collected to justify the effectiveness of the proposed approach. Empirical results demonstrate that the direct strategy performs better than iterative one in long term prediction case while iterative one performs better in the case of short term prediction. Furthermore, both deseasonalization and detrending can significantly improve the prediction accuracy for both strategies, indicating the necessity of data preprocessing. As such, this study contributes as a full reference to the planners from air transportation industries on how to tackle multistep-ahead prediction tasks in the implementation of either prediction strategy.

scholarly journals Novel Robust Time Series Analysis for Long-term and Short-term Prediction

2021 ◽  
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

Abstract Nonlinear 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 traditional least squares and least absolute deviations methods 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 the other methods fail to estimate autocorrelation accurately.

Long term prediction of forest resources

1985 ◽  
Vol 12 ◽  
pp. 176
Author(s):  
H Kurth ◽  
B Anders ◽  
G Lucas
Keyword(s):  
Forest Resources ◽  
Term Prediction ◽  
Long Term Prediction
Sign in / Sign up

Export Citation Format

Share Document