A simulation study on methods for modelling lagged associations in environmental time series data

2016 ◽  
Vol 2016 (1) ◽  
Author(s):  
Antonio Gasparrini*
Keyword(s):  
Time Series ◽  
Simulation Study ◽  
Time Series Data ◽  
Series Data ◽  
Environmental Time Series

On case-crossover methods for environmental time series data

2007 ◽  
Vol 18 (2) ◽  
pp. 157-171 ◽  
Author(s):  
Heather J. Whitaker ◽  
Mounia N. Hocine ◽  
C. Paddy Farrington
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Case Crossover ◽  
Environmental Time Series

A Comparative Simulation Study of ARIMA and Fuzzy Time Series Model for Forecasting Time Series Data

2018 ◽  
pp. 49-56
Author(s):  
Haji A. Haji ◽  
Kusman Sadik ◽  
Agus Mohamad Soleh
Keyword(s):  
Time Series ◽  
Simulation Study ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Time Series Model ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Percentage Error ◽  
Data Generation ◽  
Data Set

Simulation study is used when real world data is hard to find or time consuming to gather and it involves generating data set by specific statistical model or using random sampling. A simulation of the process is useful to test theories and understand behavior of the statistical methods. This study aimed to compare ARIMA and Fuzzy Time Series (FTS) model in order to identify the best model for forecasting time series data based on 100 replicates on 100 generated data of the ARIMA (1,0,1) model.There are 16 scenarios used in this study as a combination between 4 data generation variance error values (0.5, 1, 3,5) with 4 ARMA(1,1) parameter values. Furthermore, The performances were evaluated based on three metric mean absolute percentage error (MAPE),Root mean squared error (RMSE) and Bias statistics criterion to determine the more appropriate method and performance of model. The results of the study show a lowest bias for the chen fuzzy time series model and the performance of all measurements is small then other models. The results also proved that chen method is compatible with the advanced forecasting techniques in all of the consided situation in providing better forecasting accuracy.

Accessing environmental time series data in R from Sensor Observation Services with ease

2020 ◽  
Author(s):  
Daniel Nüst ◽  
Eike H. Jürrens ◽  
Benedikt Gräler ◽  
Simon Jirka
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Data Access ◽  
R Package ◽  
Series Data ◽  
Observation Data ◽  
Software Environment ◽  
Technical Standards ◽  
Seamless Integration ◽  
Environmental Time Series

<p>Time series data of in-situ measurements is the key to many environmental studies. The first challenge in any analysis typically arises when the data needs to be imported into the analysis framework. Standardisation is one way to lower this burden. Unfortunately, relevant interoperability standards might be challenging for non-IT experts as long as they are not dealt with behind the scenes of a client application. One standard to provide access to environmental time series data is the Sensor Observation Service (SOS, ) specification published by the Open Geospatial Consortium (OGC). SOS instances are currently used in a broad range of applications such as hydrology, air quality monitoring, and ocean sciences. Data sets provided via an SOS interface can be found around the globe from Europe to New Zealand.</p><p>The R package sos4R (Nüst et al., 2011) is an extension package for the R environment for statistical computing and visualization (), which has been demonstrated a a powerful tools for conducting and communicating geospatial research (cf. Pebesma et al., 2012; ). sos4R comprises a client that can connect to an SOS server. The user can use it to query data from SOS instances using simple R function calls. It provides a convenience layer for R users to integrate observation data from data access servers compliant with the SOS standard without any knowledge about the underlying technical standards. To further improve the usability for non-SOS experts, a recent update to sos4R includes a set of wrapper functions, which remove complexity and technical language specific to OGC specifications. This update also features specific consideration of the OGC SOS 2.0 Hydrology Profile and thereby opens up a new scientific domain.</p><p>In our presentation we illustrate use cases and examples building upon sos4R easing the access of time series data in an R and Shiny () context. We demonstrate how the abstraction provided in the client library makes sensor observation data for accessible and further show how sos4R allows the seamless integration of distributed observations data, i.e., across organisational boundaries, into transparent and reproducible data analysis workflows.</p><p><strong>References</strong></p><p>Nüst D., Stasch C., Pebesma E. (2011) Connecting R to the Sensor Web. In: Geertman S., Reinhardt W., Toppen F. (eds) Advancing Geoinformation Science for a Changing World. Lecture Notes in Geoinformation and Cartography, Springer. </p><p>Pebesma, E., Nüst, D., & Bivand, R. (2012). The R software environment in reproducible geoscientific research. Eos, Transactions American Geophysical Union, 93(16), 163–163. </p>

scholarly journals Over-Differencing and Forecasting with Non-Stationary Time Series Data

2019 ◽  
Vol 67 (1) ◽  
pp. 21-26
Author(s):  
Zakir Hossain ◽  
Atikur Rahman ◽  
Moyazzem Hossain ◽  
Jamil Hasan Karami
Keyword(s):  
Time Series ◽  
Simulation Study ◽  
Time Series Data ◽  
House Prices ◽  
Stationary Time Series ◽  
Series Data ◽  
Common Phenomenon ◽  
Stationary Time ◽  
True Model ◽  
Simulation Results

In time series analysis, over-differencing is a common phenomenon to make the data to be stationary. However, it is not always a good idea to take over-differencing in order to ensure the stationarity of time series data. In this paper, the effect of over-differencing has been investigated via a simulation study to observe how far or how close the fitted model from the true one. Simulation results show that the fitted model is found to be different and very far from the true model because of over-differencing in most of the scenarios considered in this study. In practice, it may be worthy to consider differencing as well as suitable transformation of the time series data to make it stationary. Both transformation and differencing are used for a non-stationary time series data on average monthly house prices to ensure it to be stationary. We then analyse the data and make prediction for the future values. Dhaka Univ. J. Sci. 67(1): 21-26, 2019 (January)

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data
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