scholarly journals Expectation-maximization analysis of spatial time series

2007 ◽  
Vol 14 (1) ◽  
pp. 73-77 ◽  
Author(s):  
K. W. Smith ◽  
A. L. Aretxabaleta
Keyword(s):  
Time Series ◽  
Expectation Maximization ◽  
Time Series Data ◽  
Gaussian Mixture ◽  
Sea Surface ◽  
Series Data ◽  
Eof Analysis ◽  
Orthogonal Function ◽  
The Em Algorithm ◽  
Em Method

Abstract. Expectation maximization (EM) is used to estimate the parameters of a Gaussian Mixture Model for spatial time series data. The method is presented as an alternative and complement to Empirical Orthogonal Function (EOF) analysis. The resulting weights, associating time points with component distributions, are used to distinguish physical regimes. The method is applied to equatorial Pacific sea surface temperature data from the TAO/TRITON mooring time series. Effectively, the EM algorithm partitions the time series into El Niño, La Niña and normal conditions. The EM method leads to a clearer interpretation of the variability associated with each regime than the basic EOF analysis.

scholarly journals ANALISIS KORELASI CURAH HUJAN DAN SUHU PERMUKAAN LAUT WILAYAH INDONESIA, SERTA IMPLIKASINYA UNTUK PRAKIRAAN CURAH HUJAN (STUDI KASUS KABUPATEN CILACAP) (CORRELATION ANALYSIS OF RAINFALL AND INDONESIA SEA SURFACE TEMPERATURE, AND ITS ...

Agromet ◽  
2007 ◽  
Vol 21 (2) ◽  
pp. 46 ◽  
Author(s):  
W. Estiningtyas ◽  
F. Ramadhani ◽  
E. Aldrian
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Negative Correlation ◽  
Prediction Models ◽  
Time Series Data ◽  
Sea Surface ◽  
Rainfall Pattern ◽  
Series Data ◽  
Rainfall Prediction

<p>Significant decrease in rainfall caused extreme climate has significant impact on agriculture sector, especialy food crops production. It is one of reason and push developing of rainfall prediction models as anticipate from extreme climate events. Rainfall prediction models develop base on time series data, and then it has been included anomaly aspect, like rainfall prediction model with Kalman filtering method. One of global parameter that has been used as climate anomaly indicator is sea surface temperature. Some of research indicate, there are relationship between sea surface temperature and rainfall. Relationship between Indonesian rainfall and global sea surface temperature has been known, but its relationship with Indonesian’s sea surface temperature not know yet, especialy for rainfall in smaller area like district. So, therefore the research about relationship between rainfall in distric area and Indonesian’s sea surface temperature and it application for rainfall prediction is needed. Based on Indonesian’s sea surface temperature time series data Januari 1982 until Mei 2006 show there are zona of Indonesian’s sea surface temperature (with temperature more than 27,6 0C) dominan in Januari-Mei and moved with specific pattern. Highest value of spasial correlation beetwen Cilacap’s rainfall and Indonesian’s sea surface temperature is 0,30 until 0,50 with different zona of Indonesian’s sea surface temperature. Highest positive correlation happened in March and July. Negative correlation is -0,30 until -0,70 with highest negative correlation in May and June. Model validation resulted correlation coeffcient 85,73%, fits model 20,74%, r2 73,49%, RMSE 20,5% and standart deviation 37,96. Rainfall prediction Januari-Desember 2007 period indicated rainfall pattern is near same with average rainfall pattern, rainfall less than 100/month. The result of this research indicate Indonesian’s sea surface temperature can be used as indicator rainfall condition in distric area, that means rainfall in district area can be predicted based on Indonesian’s sea surface temperature in zona with highest correlation in every month.</p><p>------------------------------------------------------------------</p><p>Penurunan curah hujan yang cukup signifikan akibat iklim ekstrim telah membawa dampak yang cukup signifikan pula pada sektor pertanian, terutama produksi tanaman pangan. Hal ini menjadi salah satu alasan yang mendorong semakin berkembangnya model-model prakiraan hujan sebagai upaya antipasi terhadap kejadian iklim ekstrim. Model prakiraan hujan yang pada awalnya hanya berbasis pada data time series, kini telah berkembang dengan memperhitungkan aspek anomali iklim, seperti model prakiraan hujan dengan metode filter Kalman. Salah satu indikator global yang dapat digunakan sebagai indikator anomali iklim adalah suhu permukaan laut. Dari berbagai hasil penelitian diketahui bahwa suhu permukaan laut ini memiliki keterkaitan dengan kejadian curah hujan. Hubungan curah hujan Indonesia dengan suhu permukaan laut global sudah banyak diketahui, tetapi keterkaitannya dengan suhu permukaan laut wilayah Indonesia belum banyak mendapat perhatian, terutama untuk curah hujan pada cakupan yang lebih sempit seperti kabupaten. Oleh karena itu perlu dilakukan penelitian yang mengkaji hubungan kedua parameter tersebut serta mengaplikasikannya untuk prakiraan curah hujan pada wilayah Kabupaten. Hasil penelitian berdasarkan data suhu permukaan laut wilayah Indonesia rata-rata Januari 1982 hingga Mei 2006 menunjukkan zona dengan suhu lebih dari 27,6 0C yang dominan pada bulan Januari-Mei dan bergerak dengan pola yang cukup jelas. Korelasi spasial antara curah hujan kabupaten Cilacap dengan SPL wilayah Indonesia rata-rata bulan Januari-Desember menunjukkan korelasi positip tertinggi antara 0,30 hingga 0,50 dengan zona SPL yang beragam. Korelasi tertinggi terjadi pada bulan Maret dan Juli. Sedangkan korelasi negatip berkisar antara -0,30 hingga -0,70 dengan korelasi negatip tertinggi pada bulan Mei dan Juni. Validasi model prakiraan hujan menghasilkan nilai koefisien korelasi 85,73%, fits model 20,74%, r2 sebesar 73,49%, RMSE 20,5% dan standar deviasi 37,96. Hasil prakiraan hujan bulanan periode Januari-Desember 2007 mengindikasikan pola curah hujan yang tidak jauh berbeda dengan rata-rata selama 19 tahun (1988-2006) dengan jeluk hujan kurang dari 100 mm/bulan. Hasil penelitian mengindikasikan bahwa SPL wilayah Indonesia dapat digunakan sebagai indikator untuk menunjukkan kondisi curah hujan di suatu wilayah (kabupaten), artinya curah hujan dapat diprediksi berdasarkan perubahan SPL pada zona-zona dengan korelasi yang tertinggi pada setiap bulannya.</p>

Pemodelan dan Peramalan Deret Waktu Studi Kasus: Suhu Permukaan Laut di Selatan Jawa Timur

2017 ◽  
Vol 1 (2) ◽  
pp. 187-199
Author(s):  
Hutomo Atman Maulana ◽  
Muliah Muliah ◽  
Maria Zefaya Sampe ◽  
Farrah Hanifah
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sample Size ◽  
Time Series Data ◽  
Temperature Time Series ◽  
Sea Surface ◽  
Series Data ◽  
Forecast Time ◽  
Temperature Time

The sea surface temperature is one of the important components that can determine the potential of the sea. This research aims to model and forecast time series data of sea surface temperature by using a Box-Jenkins method. Data in this research are the sea surface temperatures in the South of East Java (January 1983-December 2013) with sample size of 372. 360 data will be used for modeling which is from January 1983 to December 2012, and data in 2013 will be used for forecasting. Based on the results of analysis time series, the appropriate models is SARIMA(1,0,0) (1,0,1)12 where can be written as Yt = 0,010039 + 0,734220Yt−1 + 0,014893Yt−12 − (0,734220)(0,014893)Yt−13 + 0,940726et−12 with  MSE of 0.07888096.Keywords: Sea surface temperature, time series, Box-Jenkins method

scholarly journals Sea Surface Temperature Variability in the Florida Keys and Its Relationship to Coral Cover

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
I. M. Soto ◽  
F. E. Muller Karger ◽  
P. Hallock ◽  
C. Hu
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Time Series Data ◽  
Coral Cover ◽  
Monitoring Program ◽  
Florida Keys ◽  
Sea Surface ◽  
Series Data ◽  
Sst Variability

The hypothesis that moderate variability in Sea Surface Temperature (SST) is associated with higher coral cover and slower rates of decline of coral cover within the Florida Keys National Marine Sanctuary (FKNMS) was examined. Synoptic SST time series covering the period 1994–2008 were constructed for the FKNMS with the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer satellite sensors. The SST data were compared with coral-cover time-series data from 36 sites monitored by the Coral Reef and Evaluation Monitoring Program. Sites that experienced moderately high SST variability relative to other sites showed a trend toward higher percentage coral cover in 2008 and relatively slower rates of decline over the 14-year study period. The results suggest that corals at sites that are continuously exposed to moderate variability in temperature are more resilient than corals typically exposed either to low variability or to extremes.

scholarly journals A METHOD OF TIME-SERIES CHANGE DETECTION USING FULL POLSAR IMAGES FROM DIFFERENT SENSORS

2018 ◽  
Vol XLII-3 ◽  
pp. 1127-1133
Author(s):  
W. Liu ◽  
J. Yang ◽  
J. Zhao ◽  
H. Shi ◽  
L. Yang
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Time Series Data ◽  
Gaussian Mixture ◽  
Detection Methods ◽  
Series Data ◽  
Difference Image ◽  
Novel Method ◽  
Difference Images ◽  
The City

Most of the existing change detection methods using full polarimetric synthetic aperture radar (PolSAR) are limited to detecting change between two points in time. In this paper, a novel method was proposed to detect the change based on time-series data from different sensors. Firstly, the overall difference image of a time-series PolSAR was calculated by ominous statistic test. Secondly, difference images between any two images in different times ware acquired by R<sub>j</sub> statistic test. Generalized Gaussian mixture model (GGMM) was used to obtain time-series change detection maps in the last step for the proposed method. To verify the effectiveness of the proposed method, we carried out the experiment of change detection by using the time-series PolSAR images acquired by Radarsat-2 and Gaofen-3 over the city of Wuhan, in China. Results show that the proposed method can detect the time-series change from different sensors.

scholarly journals Discovering Key Transcriptomic Regulators in Pancreatic Ductal Adenocarcinoma using Dirichlet Process Gaussian Mixture Model

2020 ◽  
Author(s):  
Sk Md Mosaddek Hossain ◽  
Aanzil Akram Halsana ◽  
Lutfunnesa Khatun ◽  
Sumanta Ray ◽  
Anirban Mukhopadhyay
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Mixture Model ◽  
Dirichlet Process ◽  
Time Series Data ◽  
Gaussian Mixture ◽  
Ductal Adenocarcinoma ◽  
Series Data ◽  
Main Text

ABSTRACTPancreatic Ductal Adenocarcinoma (PDAC) is the most lethal type of pancreatic cancer (PC), late detection of which leads to its therapeutic failure. This study aims to find out key regulatory genes and their impact on the progression of the disease helping the etiology of the disease which is still largely unknown. We leverage the landmark advantages of time-series gene expression data of this disease, and thereby the identified key regulators capture the characteristics of gene activity patterns in the progression of the cancer. We have identified the key modules and predicted gene functions of top genes from the compiled gene association network (GAN). Here, we have used the natural cubic spline regression model (splineTimeR) to identify differentially expressed genes (DEG) from the PDAC microarray time-series data downloaded from gene expression omnibus (GEO). First, we have identified key transcriptomic regulators (TR) and DNA binding transcription factors (DbTF). Subsequently, the Dirichlet process and Gaussian process (DPGP) mixture model is utilized to identify the key gene modules. A variation of the partial correlation method is utilized to analyze GAN, which is followed by a process of gene function prediction from the network. Finally, a panel of key genes related to PDAC is highlighted from each of the analyses performed.Please note: Abbreviations should be introduced at the first mention in the main text – no abbreviations lists. Suggested structure of main text (not enforced) is provided below.

A hybrid CNN-LSTM based model for the prediction of sea surface temperature using time-series satellite data.

2020 ◽  
Author(s):  
Pavan Kumar Jonnakuti ◽  
Udaya Bhaskar Tata Venkata Sai
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Satellite Data ◽  
Time Series Data ◽  
Sea Surface ◽  
Machine Learning Techniques ◽  
Global Ocean ◽  
Series Data

&lt;p&gt;Sea surface temperature (SST) is a key variable of the global ocean, which affects air-sea interaction processes. Forecasts based on statistics and machine learning techniques did not succeed in considering the spatial and temporal relationships of the time series data. Therefore, to achieve precision in SST prediction we propose a deep learning-based model, by which we can produce a more realistic and accurate account of SST &amp;#8216;behavior&amp;#8217; as it focuses both on space and time. Our hybrid CNN-LSTM model uses multiple processing layers to learn hierarchical representations by implementing 3D and 2D convolution neural networks as a method to better understand the spatial features and additionally we use LSTM to examine the temporal sequence of relations in SST time-series satellite data. Widespread studies, based on the historical satellite datasets spanning from 1980 - present time, in Indian Ocean region shows that our proposed deep learning-based CNN-LSTM model is extremely capable for short and mid-term daily SST prediction accurately exclusive based on the error estimates (obtained from LSTM) of the forecasted data sets.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords: Deep Learning, Sea Surface Temperature, CNN, LSTM, Prediction.&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals RECONSTRUCTION OF SEAWATER d18O SIGNAL FROM CORAL d18O: A RECORD FROM BALI CORAL, INDONESIA

2016 ◽  
Vol 25 (1) ◽  
pp. 23
Author(s):  
Sri Yudawati Cahyarini
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Time Series Data ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Series Data ◽  
Surface Salinity ◽  
Decadal Scale ◽  
Long Time

Sea surface salinity (SSS) and precipitation are important climate (paleoclimate) parameters. To obtain long time series data of SSS/precipitation one use coral proxy. In this study, seawater d18O is extracted from d18O content in Bali coral using centering method. The result shows more convincing that d18Obali is influenced by both seawater d18O and sea surface temperature (SST). In the interannual/decadal scale the variation d18Obali clearly shows the variation of seawater d18O, it is supposed that highly variation of precipitation contribute to the seawater d18O variation which mirrored by coral d18Obali. Keywords: coral d18O, seawater d18O, precipitation, sea surface salinity, sea surface temperature Salinitas permukaan laut (SSS) dan curah hujan merupakan parameter penting untuk studi iklim maupun paleoiklim (iklim masa lampau). Untuk mendapatkan data dalam urut-urutan waktu (timeseries) yang panjang dari SSS dan curah hujan diperlukan data proksi geokimia dalam koral. Dalam studi ini kandungan d18O dalam air laut dapat di rekonstruksi dari kandungan d18O dalam koral dengan menggunakan metode centering. Hasilnya menunjukkan bahwa d18O dalam koral dipengaruhi oleh kandungan d18O dalam air laut dan SST. Dalam resolusi tahunan dan puluhan tahunan variasi d18Obali dalam koral menunjukkan dengan jelas variasi d18O dalam air laut, hal ini diduga bahwa dalam resolusi tahunan dan puluhan tahunan variasi curah hujan sangat tinggi yang berkontribusi pada tingginya variasi d18Obali dalam air laut sehingga dapat terekam oleh koral. Kata kunci: d18O koral, d18O air laut, curah hujan, salinitas permukaan laut, suhu permukaan laut.

Sign in / Sign up

Export Citation Format

Share Document