Chart Pattern in Climate Time Series Data

2016 ◽  
Author(s):  
Joachim Kaiser
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Climate Time Series

scholarly journals Missing data imputation of high‐resolution temporal climate time series data

2020 ◽  
Vol 27 (1) ◽  
Author(s):  
E Afrifa‐Yamoah ◽  
U. A. Mueller ◽  
S. M. Taylor ◽  
A. J. Fisher
Keyword(s):  
Time Series ◽  
Missing Data ◽  
High Resolution ◽  
Time Series Data ◽  
Series Data ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Climate Time Series

scholarly journals Phenological Characterization of Desert Sky Island Vegetation Communities with Remotely Sensed and Climate Time Series Data

2010 ◽  
Vol 2 (2) ◽  
pp. 388-415 ◽  
Author(s):  
Willem J.D. Van Leeuwen ◽  
Jennifer E. Davison ◽  
Grant M. Casady ◽  
Stuart E. Marsh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Remotely Sensed ◽  
Series Data ◽  
Vegetation Communities ◽  
Climate Time Series ◽  
Sky Island

scholarly journals Deep learning classification of temporal data in ecology

2020 ◽  
Author(s):  
César Capinha ◽  
Ana Ceia-Hasse ◽  
Andrew M. Kramer ◽  
Christiaan Meijer
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Meteorological Data ◽  
Species Distribution Modelling ◽  
Series Data ◽  
Temporal Data ◽  
Climate Time Series ◽  
Biological Entities

AbstractTemporal data is ubiquitous in ecology and ecologists often face the challenge of accurately differentiating these data into predefined classes, such as biological entities or ecological states. The usual approach transforms the temporal data into static predictors of the classes. However, recent deep learning techniques can perform the classification using raw time series, eliminating subjective and resource-consuming data transformation steps, and potentially improving classification results. We present a general approach for time series classification that considers multiple deep learning algorithms and illustrate it with three case studies: i) insect species identification from wingbeat spectrograms; ii) species distribution modelling from climate time series and iii) the classification of phenological phases from continuous meteorological data. The deep learning approach delivered ecologically sensible and accurate classifications, proving its potential for wide applicability across subfields of ecology. We recommend deep learning as an alternative to techniques requiring the transformation of time series data.

scholarly journals On the influence of spatial sampling on climate networks

2014 ◽  
Vol 21 (3) ◽  
pp. 651-657 ◽  
Author(s):  
N. Molkenthin ◽  
K. Rehfeld ◽  
V. Stolbova ◽  
L. Tupikina ◽  
J. Kurths
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Voronoi Cell ◽  
Spatial Sampling ◽  
Series Data ◽  
Cell Size Distribution ◽  
Reconstructed Network ◽  
Climate Time Series ◽  
The Impact ◽  
Climate Networks

Abstract. Climate networks are constructed from climate time series data using correlation measures. It is widely accepted that the geographical proximity, as well as other geographical features such as ocean and atmospheric currents, have a large impact on the observable time-series similarity. Therefore it is to be expected that the spatial sampling will influence the reconstructed network. Here we investigate this by comparing analytical flow networks, networks generated with the START model and networks from temperature data from the Asian monsoon domain. We evaluate them on a regular grid, a grid with added random jittering and two variations of clustered sampling. We find that the impact of the spatial sampling on most network measures only distorts the plots if the node distribution is significantly inhomogeneous. As a simple diagnostic measure for the detection of inhomogeneous sampling we suggest the Voronoi cell size distribution.

scholarly journals FORECASTING OF TEMPERATURE IN BANGLADESH BY USING SLICED FUNCTIONAL TIME SERIES (SFTS) APPROACH

2020 ◽  
Vol 5 (10) ◽  
Author(s):  
FARHANA AKTER BINA
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Prediction Intervals ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Temperature Forecast ◽  
Functional Time Series ◽  
Climate Time Series

Climate is a paradigm of a complex system and its changes are global in nature. It is an exciting challenge to predict these changes over the period of different time scales. Time series analysis is one of the most important and major tools to analyze the climate time series data. Temperature is one of the most important climatic parameter. In this research, our main aim is to conduct a study across the country to forecast temperature through a relatively new method of forecasting approach named as sliced functional time series (SFTS). The monthly forecasts were obtained along with prediction intervals. These forecasts were compared with the forecasts obtained from autoregressive integrated moving average (ARIMA) and exponential smoothing state-space (ETS) models based on the accuracy measures and the length of prediction intervals to evaluate the performance of SFTS approach. Keywords: Climate,Functional Time Series,Sliced Functional Time Series, Temperature, Forecast, Forecast Accuracy

scholarly journals An expandable web-based platform for visually analyzing basin-scale hydro-climate time series data

2016 ◽  
Vol 78 ◽  
pp. 97-105 ◽  
Author(s):  
Joeseph P. Smith ◽  
Timothy S. Hunter ◽  
Anne H. Clites ◽  
Craig A. Stow ◽  
Tad Slawecki ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Web Based ◽  
Basin Scale ◽  
Climate Time Series

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

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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