scholarly journals Statistical Analysis of Cropland Area in Canada using the Autoregressive Hidden Markov Time Series Model

2021 ◽  
Vol 20 ◽  
pp. 615-624
Author(s):  
Lanrewaju O. Adekola
Keyword(s):  
Land Use ◽  
Time Series ◽  
Crop Production ◽  
Hidden Markov ◽  
Developed Countries ◽  
Time Series Model ◽  
Model Parameters ◽  
Agricultural Activities ◽  
Stationary Probability Distribution ◽  
Cropland Area

Crop production and other agricultural activities are as old as human existence and becoming increasingly intensive, spatially concentrated and specialized. However, diversification in economic activities and recent development in technology in many developed countries have led to significant increase in land use. Thereby, resulting to huge reduction in the total land area available for agricultural activities especially crop production. This study examines the distribution of cropland area in Canada in relation to three contributing factors using the Autoregressive Hidden Markov time series Model (AR-HMM) due to the limitations of the ordinary Autoregressive model in the accuracy of its parameter estimation. Expectation-Maximization (E-M) algorithm method was used to estimate the model parameters so as to investigate the effects of the factors on cropland distribution using secondary data from Food and Agriculture Organisation (FAO). Jarque-Bera and D'Agostino normality tests were carried out to examine the normality of the series. Augmented Dickey Fuller (ADF) and the KPSS tests established the stationarity of the series. The ideal stationary probability distribution for transition was at AR (3)-HMM with the minimum Bayesian Information Criterion (BIC) of 16270.62. The prior transition states for the HMM are 0.462, 0.260 and 0.278 respectively. In conclusion, this study suggests that deforestation and other land use activities as a result of commercial and technological advancements should be minimized to ensure more available cropland area.

scholarly journals Detection of contributing object to driving operations based on hidden Markov model

2019 ◽  
Vol 16 (5) ◽  
pp. 172988141987679
Author(s):  
Kohjiro Hashimoto ◽  
Tetsuyasu Yamada ◽  
Takeshi Tsuchiya ◽  
Kae Doki ◽  
Yuki Funabora ◽  
...  
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Cognitive Performance ◽  
Traffic Accidents ◽  
Hidden Markov ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Cognitive Support ◽  
Object Based

With increase in the number of elderly people in the Japanese society, traffic accidents caused by elderly driver is considered problematic. The primary factor of the traffic accidents is a reduction in their driving cognitive performance. Therefore, a system that supports the cognitive performance of drivers can greatly contribute in preventing accidents. Recently, the development of devices for visually providing information, such as smart glasses or head up display, is in progress. These devices can provide more effective supporting information for cognitive performance. In this article, we focus on the selection problem of information to be presented for drivers to realize the cognitive support system. It has been reported that the presentation of excessive information to a driver reduces the judgment ability of the driver and makes the information less trustworthy. Thus, indiscriminate presentation of information in the vision of the driver is not an effective cognitive support. Therefore, a mechanism for determining the information to be presented to the driver based on the current driving situation is required. In this study, the object that contributes to execution of avoidance driving operation is regarded as the object that drivers must recognize and present for drivers. This object is called as contributing object. In this article, we propose a method that selects contributing objects among the appeared objects on the current driving scene. The proposed method expresses the relation between the time series change of an appeared object and avoidance operation of the driver by a mathematical model. This model can predict execution timing of avoidance driving operation and estimate contributing object based on the prediction result of driving operation. This model named as contributing model consisted of multi-hidden Markov models. Hidden Markov model is time series probabilistic model with high readability. This is because that model parameters express the probabilistic distribution and its statistics. Therefore, the characteristics of contributing model are that it enables the designer to understand the basis for the output decision. In this article, we evaluated detection accuracy of contributing object based on the proposed method, and readability of contributing model through several experiments. According to the results of these experiments, high detection accuracy of contributing object was confirmed. Moreover, it was confirmed that the basis of detected contributing object judgment can be understood from contributing model.

scholarly journals Cropland area estimates using Modis NDVI time series in the state of Mato Grosso, Brazil

2012 ◽  
Vol 47 (9) ◽  
pp. 1270-1278 ◽  
Author(s):  
Daniel de Castro Victoria ◽  
Adriano Rolim da Paz ◽  
Alexandre Camargo Coutinho ◽  
Jude Kastens ◽  
J. Christopher Brown
Keyword(s):  
Time Series ◽  
Crop Production ◽  
Vegetation Index ◽  
The State ◽  
Mato Grosso ◽  
Cropland Area ◽  
Modis Ndvi ◽  
Ndvi Time Series ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Production Areas

The objective of this work was to evaluate a simple, semi‑automated methodology for mapping cropland areas in the state of Mato Grosso, Brazil. A Fourier transform was applied over a time series of vegetation index products from the moderate resolution imaging spectroradiometer (Modis) sensor. This procedure allows for the evaluation of the amplitude of the periodic changes in vegetation response through time and the identification of areas with strong seasonal variation related to crop production. Annual cropland masks from 2006 to 2009 were generated and municipal cropland areas were estimated through remote sensing. We observed good agreement with official statistics on planted area, especially for municipalities with more than 10% of cropland cover (R² = 0.89), but poor agreement in municipalities with less than 5% crop cover (R² = 0.41). The assessed methodology can be used for annual cropland mapping over large production areas in Brazil.

scholarly journals Identifying dynamical time series model parameters from equilibrium samples, with application to gene regulatory networks

2018 ◽  
Vol 19 (4) ◽  
pp. 444-465
Author(s):  
William Chad Young ◽  
Ka Yee Yeung ◽  
Adrian E Raftery
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Steady State ◽  
Gene Expression Data ◽  
Regulatory Networks ◽  
Network Inference ◽  
Time Series Model ◽  
Model Parameters ◽  
Expression Data ◽  
Gene Regulatory

Gene regulatory network reconstruction is an essential task of genomics in order to further our understanding of how genes interact dynamically with each other. The most readily available data, however, are from steady-state observations. These data are not as informative about the relational dynamics between genes as knockout or over-expression experiments, which attempt to control the expression of individual genes. We develop a new framework for network inference using samples from the equilibrium distribution of a vector autoregressive (VAR) time-series model which can be applied to steady-state gene expression data. We explore the theoretical aspects of our method and apply the method to synthetic gene expression data generated using GeneNetWeaver.

scholarly journals Structural Damage Assessment by Using Stiffness-based Method and Cubic Spline Interpolation

2018 ◽  
Vol 201 ◽  
pp. 01004
Author(s):  
Wei-Chih Su ◽  
Chiung-Shiann Huang ◽  
Jyh-Horng Wu
Keyword(s):  
Time Series ◽  
Stiffness Matrix ◽  
Spline Interpolation ◽  
Coefficient Matrix ◽  
Cubic Spline ◽  
Time Series Model ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Structural Dynamic ◽  
Cubic Spline Interpolation

This study explores the possibility of using stiffness-based method and cubic spline interpolation to locate the damaged storey of a building during a strong earthquake, and corresponding stiffness matrix of structure often change in the earthquake process. The time series model of a building is established from the full structural dynamic responses. Next, the coefficient matrix of the time series model could be solved by recursive least squares (RLS) algorithms. Then, the model parameters of a building are calculated by the coefficient matrix of time series model. Finally, the identified natural frequencies and mode shapes of structure that corrected by cubic spline interpolation would be used to construct the stiffness matrix of a building. Then, the damage location of a building could be detected by the identified stiffness matrix of a building. The effectiveness of the proposed procedure is verified using numerically simulated earthquake responses of the finite element model of a six-storey frame.

scholarly journals Activation Detection on fMRI Time Series Using Hidden Markov Model

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Rong Duan ◽  
Hong Man
Keyword(s):  
Time Series ◽  
Brain Tumor ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Viterbi Algorithm ◽  
Hidden Markov ◽  
Gaussian Model ◽  
Model Parameters ◽  
Ratio Test ◽  
Activation Detection

This paper introduces two unsupervised learning methods for analyzing functional magnetic resonance imaging (fMRI) data based on hidden Markov model (HMM). HMM approach is focused on capturing the first-order statistical evolution among the samples of a voxel time series, and it can provide a complimentary perspective of the BOLD signals. Two-state HMM is created for each voxel, and the model parameters are estimated from the voxel time series and the stimulus paradigm. Two different activation detection methods are presented in this paper. The first method is based on the likelihood and likelihood-ratio test, in which an additional Gaussian model is used to enhance the contrast of the HMM likelihood map. The second method is based on certain distance measures between the two state distributions, in which the most likely HMM state sequence is estimated through the Viterbi algorithm. The distance between the on-state and off-state distributions is measured either through a t-test, or using the Kullback-Leibler distance (KLD). Experimental results on both normal subject and brain tumor subject are presented. HMM approach appears to be more robust in detecting the supplemental active voxels comparing with SPM, especially for brain tumor subject.

scholarly journals A Bayesian stochastic generator to complement existing climate change scenarios: supporting uncertainty quantification in marine and coastal ecosystems

2020 ◽  
Author(s):  
Lőrinc Mészáros ◽  
Frank van der Meulen ◽  
Geurt Jongbloed ◽  
Ghada El Serafy
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Coastal Ecosystems ◽  
Time Series Model ◽  
Ecological Impacts ◽  
Time Shift ◽  
Model Parameters ◽  
Climate Change Scenarios ◽  
Climate Variables ◽  
Residual Term

AbstractAvailable climate change projections, which can be used for quantifying future changes in marine and coastal ecosystems, usually consist of a few scenarios. Studies addressing ecological impacts of climate change often make use of a low- (RCP2.6), moderate- (RCP4.5) or high climate scenario (RCP8.5), without taking into account further uncertainties in these scenarios. In this research a methodology is proposed to generate further synthetic scenarios, based on existing datasets, for a better representation of climate change induced uncertainties. The methodology builds on Regional Climate Model scenarios provided by the EURO-CORDEX experiment. In order to generate new realizations of climate variables, such as radiation or temperature, a hierarchical Bayesian model is developed. In addition, a parameterized time series model is introduced, which includes a linear trend component, a seasonal shape with varying amplitude and time shift, and an additive residual term. The seasonal shape is derived with the non-parametric locally weighted scatterplot smoothing, and the residual term includes the smoothed variance of residuals and independent and identically distributed noise. The distributions of the time series model parameters are estimated through Bayesian parameter inference with Markov chain Monte Carlo sampling (Gibbs sampler). By sampling from the predictive distribution numerous new statistically representative synthetic scenarios can be generated including uncertainty estimates. As a demonstration case, utilizing these generated synthetic scenarios and a physically based ecological model (Delft3D-WAQ) that relates climate variables to ecosystem variables, a probabilistic simulation is conducted to further propagate the climate change induced uncertainties to marine and coastal ecosystem indicators.

scholarly journals Regionalizing crop types to enhance global ecosystem modelling of maize production

2021 ◽  
Author(s):  
Yi Yang ◽  
Stephen M. Ogle ◽  
Stephen Del Grosso ◽  
Nathan Mueller ◽  
Shannon Spencer ◽  
...  
Keyword(s):  
Crop Production ◽  
Crop Yields ◽  
Mean Squared Error ◽  
Ecosystem Model ◽  
Developed Countries ◽  
Strategy Development ◽  
Single Type ◽  
Model Parameters ◽  
The World ◽  
Crop Types

Abstract Improving the prediction of crop production is critical for strategy development associated with global food security, particularly as the climate continues to change. Process-based ecosystem models are increasingly used for simulating global agricultural production. However, such simulations often use a single crop variety in global assessments, implying that major crops are identical across all regions of the world. To address this limitation, we applied a Bayesian approach to calibrate regional types of maize (Zea mays L.), capturing the aggregated traits of local varieties, for DayCent ecosystem model simulations, using global crop production data from 2001 to 2013. We selected major cropping regions from the FAO Global Agro-Environmental Stratification as a basis for the regionalization and identified the most important model parameters through a global sensitivity analysis. We calibrated DayCent using the sampling importance resampling algorithm and found significant improvement in DayCent simulations of maize yields with the calibrated regional varieties. Compared to a single type of maize for the world, the regionalization of maize leads to reductions in root mean squared error of 11%, 31%, 27%, 30%, 19%, and 27% and reductions in bias of 59%, 59%, 50%, 81%, 32%, and 56% for Africa, East Asia, Europe, North America, South America, and South & Southeast Asia, respectively. We also found the optimum parameter values of radiation use efficiency are positively correlated with the income level of different regions, which indicates that breeding has enhanced the photosynthetic efficiency of maize in developed countries. There may also be opportunities for expanding crop breeding programs in developing countries to enhance photosynthesis efficiency and reduce the yield gap in these regions. This study highlights the importance of representing regional variation in crop types for achieving accurate predictions of crop yields.

scholarly journals Combining Survey Forecasts and Time Series Models: The Case of the Euribor

2011 ◽  
Vol 231 (1) ◽  
Author(s):  
Fabian Krüger ◽  
Winfried Pohlmeier ◽  
Frieder Mokinski
Keyword(s):  
Time Series ◽  
Time Series Model ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Time Series Models ◽  
Sources Of Information ◽  
Macroeconomic Forecasting ◽  
Dynamic Algorithm ◽  
History Of ◽  
Combined Forecasts

SummaryThis paper reinterprets Maganelli’s (2009) idea of “Forecasting with Judgment” to obtain a dynamic algorithm for combining survey expectations data and time series models for macroeconomic forecasting. Existing combination approaches typically obtain combined forecasts by linearly weighting individual forecasts. The approach presented here instead uses survey forecasts in the estimation stage of a time series model. Thus an estimate of the model parameters is obtained that reflects two sources of information: a history of realizations of the variables that are involved in the time series model and survey expectations on the future course of the variable that is to be forecast. The idea at the estimation stage is to shrink parameter estimates towards values that are compatible (in an appropriate sense) with the survey forecasts that have been observed. It is exemplified how this approach can be applied to different autoregressive time series models. In an empirical application, the approach is used to forecast the three-month Euribor at a six-month horizon.

scholarly journals Feature Fusion Approach for Temporal Land Use Mapping in Complex Agricultural Areas

2021 ◽  
Vol 13 (13) ◽  
pp. 2517
Author(s):  
Lijun Wang ◽  
Jiayao Wang ◽  
Fen Qin
Keyword(s):  
Land Use ◽  
Time Series ◽  
Crop Production ◽  
Large Scale ◽  
Vegetation Index ◽  
Feature Fusion ◽  
Vegetation Indices ◽  
Google Earth ◽  
Classification Rule ◽  
Cultivated Land

Accurate temporal land use mapping provides important and timely information for decision making for large-scale management of land and crop production. At present, temporal land cover and crop classifications within a study area have neglected the differences between subregions. In this paper, we propose a classification rule by integrating the terrain, time series characteristics, priority, and seasonality (TTPSR) with Sentinel-2 satellite imagery. Based on the time series of Normalized Difference Water Index (NDWI) and Vegetation Index (NDVI), a dynamic decision tree for forests, cultivation, urban, and water was created in Google Earth Engine (GEE) for each subregion to extract cultivated land. Then, with or without this cultivated land mask data, the original classification results for each subregion were completed based on composite image acquisition with five vegetation indices using Random Forest. During the post-reclassification process, a 4-bit coding rule based on terrain, type, seasonal rhythm, and priority was generated by analyzing the characteristics of the original results. Finally, statistical results and temporal mapping were processed. The results showed that feature importance was dominated by B2, NDWI, RENDVI, B11, and B12 over winter, and B11, B12, NDBI, B2, and B8A over summer. Meanwhile, the cultivated land mask improved the overall accuracy for multicategories (seven to eight and nine to 13 during winter and summer, respectively) in each subregion, with average ranges in the overall accuracy for winter and summer of 0.857–0.935 and 0.873–0.963, respectively, and kappa coefficients of 0.803–0.902 and 0.835–0.950, respectively. The analysis of the above results and the comparison with resampling plots identified various sources of error for classification accuracy, including spectral differences, degree of field fragmentation, and planting complexity. The results demonstrated the capability of the TTPSR rule in temporal land use mapping, especially with regard to complex crops classification and automated post-processing, thereby providing a viable option for large-scale land use mapping.

PROFILE SUMMARIES FOR ARIMA TIME SERIES MODEL PARAMETERS

1991 ◽  
Vol 12 (3) ◽  
pp. 225-235 ◽  
Author(s):  
Raymond L. H. Lam ◽  
Donald G. Watts
Keyword(s):  
Time Series ◽  
Time Series Model ◽  
Model Parameters
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