The Nonlinear Least Squares Fits of Asymmetric Gaussian Model Functions: A Method for Reducing Noise in MODIS LAI Time-series Data

2006 ◽  
Author(s):  
X. Li ◽  
L. Tong ◽  
W. Xu ◽  
B. He ◽  
H. Yuan ◽  
...  
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Time Series Data ◽  
Gaussian Model ◽  
Nonlinear Least Squares ◽  
Series Data ◽  
Modis Lai

scholarly journals A Novel Time Series Prediction Approach Based on a Hybridization of Least Squares Support Vector Regression and Swarm Intelligence

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Nhat-Duc Hoang ◽  
Anh-Duc Pham ◽  
Minh-Tu Cao
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Support Vector Regression ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Hybrid Approach ◽  
Series Data ◽  
Support Vector ◽  
Promising Alternative ◽  
Prediction Approach

This research aims at establishing a novel hybrid artificial intelligence (AI) approach, named as firefly-tuned least squares support vector regression for time series prediction(FLSVRTSP). The proposed model utilizes the least squares support vector regression (LS-SVR) as a supervised learning technique to generalize the mapping function between input and output of time series data. In order to optimize the LS-SVR’s tuning parameters, theFLSVRTSPincorporates the firefly algorithm (FA) as the search engine. Consequently, the newly construction model can learn from historical data and carry out prediction autonomously without any prior knowledge in parameter setting. Experimental results and comparison have demonstrated that theFLSVRTSPhas achieved a significant improvement in forecasting accuracy when predicting both artificial and real-world time series data. Hence, the proposed hybrid approach is a promising alternative for assisting decision-makers to better cope with time series prediction.

Phenology estimation of subtropical bamboo forests based on assimilated MODIS LAI time series data

2021 ◽  
Vol 173 ◽  
pp. 262-277
Author(s):  
Xuejian Li ◽  
Huaqiang Du ◽  
Guomo Zhou ◽  
Fangjie Mao ◽  
Meng Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Modis Lai

scholarly journals Assimilating Multiresolution Leaf Area Index of Moso Bamboo Forest from MODIS Time Series Data Based on a Hierarchical Bayesian Network Algorithm

2018 ◽  
Vol 11 (1) ◽  
pp. 56 ◽  
Author(s):  
Luqi Xing ◽  
Xuejian Li ◽  
Huaqiang Du ◽  
Guomo Zhou ◽  
Fangjie Mao ◽  
...  
Keyword(s):  
Time Series ◽  
Carbon Cycle ◽  
Bayesian Network ◽  
Leaf Area Index ◽  
Time Series Data ◽  
Series Data ◽  
Bamboo Forest ◽  
Area Index ◽  
Moso Bamboo Forest ◽  
Modis Lai

The highly accurate multiresolution leaf area index (LAI) is an important parameter for carbon cycle simulation for bamboo forests at different scales. However, current LAI products have discontinuous resolution with 1 km mostly, that makes it difficult to accurately quantify the spatiotemporal evolution of carbon cycle at different resolutions. Thus, this study used MODIS LAI product (MOD15A2) and MODIS reflectance data (MOD09Q1) of Moso bamboo forest (MBF) from 2015, and it adopted a hierarchical Bayesian network (HBN) algorithm coupled with a dynamic LAI model and the PROSAIL model to obtain high-precision LAI data at multiresolution (i.e., 1000, 500, and 250 m). The results showed the LAIs assimilated using the HBN at the three resolutions corresponded with the actual growth trend of the MBF and correlated significantly with the observed LAI with a determination coefficient (R2) value of > 0.80. The highest-precision assimilated LAI was obtained at 1000-m resolution with R2 values of 0.91. The LAI assimilated using the HBN algorithm achieved better accuracy than the MODIS LAI with increases in the R2 value of 2.7 times and decreases in the root mean square error of 87.8%. Therefore, the HBN algorithm applied in this study can effectively obtain highly accurate multiresolution LAI time series data for bamboo forest.

Time Series Regression with Mixtures of Integrated Processes

1995 ◽  
Vol 11 (5) ◽  
pp. 1033-1094 ◽  
Author(s):  
Yoosoon Chang ◽  
Peter C.B. Phillips
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Time Series Data ◽  
Unit Roots ◽  
Ordinary Least Squares ◽  
Series Data ◽  
Vector Autoregressions ◽  
Time Series Regression ◽  
First Order ◽  
Unknown Mixture

The paper develops a statistical theory for regressions with integrated regressors of unknown order and unknown cointegrating dimension. In practice, we are often unsure whether unit roots or cointegration is present in time series data, and we are also uncertain about the order of integration in some cases. This paper addresses issues of estimation and inference in cases of such uncertainty. Phillips (1995, Econometrica 63, 1023–1078) developed a theory for time series regressions with an unknown mixture of 1(0) and 1(1) variables and established that the method of fully modified ordinary least squares (FM-OLS) is applicable to models (including vector autoregressions) with some unit roots and unknown cointegrating rank. This paper extends these results to models that contain some I(0), I(1), and I(2) regressors. The theory and methods here are applicable to cointegrating regressions that include unknown numbers of I(0), I(1), and I(2) variables and an unknown degree of cointegration. Such models require a somewhat different approach than that of Phillips (1995). The paper proposes a residual-based fully modified ordinary least-squares (RBFMOLS) procedure, which employs residuals from a first-order autoregression of the first differences of the entire regressor set in the construction of the FMOLS estimator. The asymptotic theory for the RBFM-OLS estimator is developed and is shown to be normal for all the stationary coefficients and mixed normal for all the nonstationary coefficients. Under Gaussian assumptions, estimation of the cointegration space by RBFM-OLS is optimal even though the dimension of the space is unknown.

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