Multiplexed space-time maps for time series data visualization: application to 4D cardiac imaging

1998 ◽  
Author(s):  
Justin D. Pearlman ◽  
Zimri Yaseen
Keyword(s):  
Time Series ◽  
Data Visualization ◽  
Cardiac Imaging ◽  
Time Series Data ◽  
Space Time ◽  
Series Data ◽  
Visualization Application

scholarly journals PEMODELAN GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) SEASONAL PADA DATA CURAH HUJAN EMPAT KABUPATEN DI PROVINSI JAWA TENGAH

2019 ◽  
Vol 8 (4) ◽  
pp. 418-427
Author(s):  
Eko Siswanto ◽  
Hasbi Yasin ◽  
Sudarno Sudarno
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Space Time ◽  
Series Data ◽  
Inverse Distance Weighted ◽  
Modeling And Forecasting ◽  
Distance Weighted ◽  
The Relationship ◽  
Inverse Distance

In many applications, several time series data are recorded simultaneously at a number of locations. Time series data from nearby locations often to be related by spatial and time. This data is called spatial time series data. Generalized Space Time Autoregressive (GSTAR) model is one of space time models used to modeling and forecasting spatial time series data. This study applied GTSAR model to modeling volume of rainfall four locations in Jepara Regency, Kudus Regency, Pati Regency, and Grobogan Regency. Based on the smallest RMSE mean of forecasting result, the best model chosen by this study is GSTAR (11)-I(1)12 with the inverse distance weighted. Based on GSTAR(11)-I(1)12 with the inverse distance weighted, the relationship between the location shown on rainfall Pati Regency influenced by the rainfall in other regencies. Keywords: GSTAR, RMSE, Rainfall

scholarly journals Pemodelan Harga Beras di Pulau Sumatera dengan Menggunakan Model Generalized Space Time ARIMA

2018 ◽  
Vol 2 (2) ◽  
pp. 49-57
Author(s):  
Dwi Yulianti ◽  
I Made Sumertajaya ◽  
Itasia Dina Sulvianti
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Space Time ◽  
Series Data ◽  
Model Parameters ◽  
Autoregressive Integrated Moving Average ◽  
Sumatra Island ◽  
Rice Price ◽  
Multiple Variables

Generalized space time autoregressive integrated  moving average (GSTARIMA) model is a time series model of multiple variables with spatial and time linkages (space time). GSTARIMA model is an extension of the space time autoregressive integrated moving average (STARIMA) model with the assumption that each location has unique model parameters, thus GSTARIMA model is more flexible than STARIMA model. The purposes of this research are to determine the best model and predict the time series data of rice price on all provincial capitals of Sumatra island using GSTARIMA model. This research used weekly data of rice price on all provincial capitals of Sumatra island from January 2010 to December 2017. The spatial weights used in this research are the inverse distance and queen contiguity. The modeling result shows that the best model is GSTARIMA (1,1,0) with queen contiguity weighted matrix and has the smallest MAPE value of 1.17817 %.

scholarly journals Model Space Time Autoregressive (STAR) dan Aplikasinya Terhadap Penyakit Demam Berdarah Dengue di Provinsi Sulawesi Barat

2019 ◽  
Vol 1 (2) ◽  
pp. 115
Author(s):  
Wahidah Sanusi ◽  
Maya Sari Wahyuni ◽  
Rahmat Setiawan
Keyword(s):  
Time Series ◽  
Dengue Fever ◽  
Time Series Data ◽  
Model Space ◽  
Space Time ◽  
Series Data ◽  
Star Model ◽  
Autoregressive Time Series ◽  
Forecast Data ◽  
Model Star

Abstrak. Model Space Time Autoregressive (STAR) merupakan data deret waktu yang mempunyai keterkaitan antar lokasi (space time). Tujuan dari penelitian ini adalah untuk mendapatkan model STAR yang sesuai dengan data jumlah penderita penyakit DBD di Provinsi Sulawesi Barat serta memperoleh data hasil ramalan untuk beberapa bulan kedepan. Data yang digunakan merupakan data bulanan penderita DBD di lima lokasi yaitu Kota Mamuju, Kabupaten Majene, Kabupaten Polmas, Kabupaten Mamuju Tengah, dan Kabupaten Mamuju Utara pada Januari 2014 sampai Juli 2016. Pendugaan parameter model STAR menggunakan metode kuadrat terkecil (MKT). Model STAR yang sesuai dengan data jumlah penderita penyakit DBD di Provinsi Sulawesi Barat adalah model STAR5(11). Pembobot yang digunakan merupakan bobot lokasi seragam. Pada hasil pengecekan parameter penduga dengan menggunakan bobot lokasi seragam didapatkan tiga model. Hal ini dilihat dari adanya pengaruh yang nyata terhadap lokasi yang berdekatan. Hasil ramalan dengan model STAR5(11) tentang jumlah penderita penyakit DBD di Provinsi Sulawesi Barat untuk dua bulan kedepan yaitu bulan Agustus sampai September 2016.Kata Kunci: Model STAR, ARIMA, Autoregressive, Deret WaktuAbstract. The Space Time Autoregressive (STAR) model is a time series data that has a link between locations (space time). The purpose of this study was to obtain a STAR model that was in accordance with the data on the number of dengue fever patients in West Sulawesi Province and also the forecast data for the next few months. Data in the form of DHF data in five locations, namely Mamuju City, Majene Regency, Polmas District, Central Mamuju Regency, and North Mamuju Regency from January 2014 to July 2016. STAR Estimation parameter model uses vertical squares (MKT) method. The STAR model that matches the data on the number of DHF patients in West Sulawesi Province is the STAR5 model (11). The weighting is a uniform location. In the estimator checking results using uniform location weight of three models. Things that happen between others. Forecast results with the STAR5 (11) model on the number of dengue fever patients in West Sulawesi Province for the next two months, namely August to September 2016, namely 9 people for Mamuju City and 12 people for Polman Regency.Keywords: STAR Model, ARIMA, Autoregressive, Time Series

scholarly journals Automation of Longitudinal Time Series Data Visualization using Plot Specifications obtained from XL file

2019 ◽  
Vol 8 (6) ◽  
pp. 3105-3109
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Data Visualization ◽  
Time Series Data ◽  
R Package ◽  
Series Data ◽  
Complex Data ◽  
Business Decision ◽  
Business Requirement ◽  
Visualization Techniques

Longitudinal Time Series data visualization plays important role in all sector of business decision making [9]. With enormous amount of complex data [11] from cloud and business requirement, number of graphs needed for decision making increased many folds. Generating enormous number of plots manually with more human input is tedious, time consuming and error prone. To avoid these issues, suitable visualization techniques with solid design principles become very important. We conceptualized and designed a novel method for automation of these processes. R-GGPLOT2[7] package and XL specifications file were primarily used to achieve this goal. We here show as how we can create multiple plots from time series data, plots specifications-XL file and R package GGPLOT2[7] in a single run. Since all required information are entered in XL sheet, R function can be run with no modification. Multiple plots can be generated by using enormous data available in production and service sectors such as finance, healthcare, transportation and food industries etc.

scholarly journals A Survey of Time Series Data Visualization Methods

2020 ◽  
Vol 2 (2) ◽  
pp. 105-117
Author(s):  
Wangdong Jiang ◽  
Jie Wu ◽  
Guang Sun ◽  
Yuxin Ouyang ◽  
Jing Li ◽  
...  
Keyword(s):  
Time Series ◽  
Data Visualization ◽  
Time Series Data ◽  
Series Data
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