Forecasting Tourist Arrivals in China Based on Seasonal Decomposition and LSSVR Model

2019 ◽  
pp. 452-459
Author(s):  
Gang Xie ◽  
Jian Zhang ◽  
Boyu Yang ◽  
Shouyang Wang
Keyword(s):  
Seasonal Decomposition

A seasonal decomposition of the estimated size of a penguin population at Phillip Island, Australia

2005 ◽  
Vol 53 (2) ◽  
pp. 111 ◽  
Author(s):  
H.-C. Yang ◽  
L. E. Chambers ◽  
R. M. Huggins
Keyword(s):  
Population Size ◽  
Sampling Methods ◽  
Seasonal Effects ◽  
Trend Component ◽  
Seasonal Time Series ◽  
The Difference ◽  
Weaker Conditions ◽  
Seasonal Decomposition ◽  
Parametric Estimate ◽  
Non Parametric

Modern non-parametric methods allow the estimation of a population size under weaker conditions than the classical methods if there are frequent capture occasions. Here a non-parametric estimate of the number of penguins occupying Summerland Beach, Phillip Island, Australia, was computed. This estimator did not assume equal catchability of individuals, did not assume a parametric form for the population size as a function of time and allowed individuals to leave and re-enter the population. The resulting estimate was then decomposed into a seasonal component and a trend component using seasonal time series models to facilitate the understanding of the changes in the population size. The estimated seasonal effects quantified the difference between the number of penguins in the breeding and non-breeding season, and the trend component indicated an increase in the population size over the period of the study. The estimates of the number of penguins are shown to be consistent with other estimates obtained from a variety of sampling methods and statistical analyses.

scholarly journals Adaptive Baseline Finder, a statistical data selection strategy to identify atmospheric CO<sub>2</sub> baseline levels and its application to European elevated mountain stations

2017 ◽  
Author(s):  
Ye Yuan ◽  
Ludwig Ries ◽  
Hannes Petermeier ◽  
Martin Steinbacher ◽  
Angel J. Gómez-Peláez ◽  
...  
Keyword(s):  
Statistical Data ◽  
Data Selection ◽  
Selection Strategy ◽  
Data Sets ◽  
Critical Data ◽  
Baseline Levels ◽  
Gas Measurements ◽  
Atmospheric Trace Gas ◽  
Seasonal Decomposition

Abstract. Critical data selection is essential for determining representative baseline levels of atmospheric trace gas measurements even at remote measuring sites. Different data selection techniques have been used around the world which could potentially lead to bias when comparing data from different stations. This paper presents a novel statistical data selection method based on CO2 diurnal pattern occurring typically at high elevated mountain stations. Its capability and applicability was studied for atmospheric measuring records of CO2 from 2010 to 2016 at six Global Atmosphere Watch (GAW) stations in Europe, namely Zugspitze-Schneefernerhaus (Germany), Sonnblick (Austria), Jungfraujoch (Switzerland), Izaña (Spain), Schauinsland (Germany) and Hohenpeissenberg (Germany). Three other frequently applied statistical data selection methods were implemented for comparison. Among all selection routines, the new method named Adaptive Baseline Finder (ABF) resulted in lower selection percentages with lower maxima during winter and higher minima during summer in the selected data. To investigate long-term trend and seasonality, seasonal decomposition technique STL was applied. Compared with the unselected data, mean annual growth rates of all selected data sets were not significantly different except for Schauinsland. However, clear differences were found in the annual amplitudes as well as for the seasonal time structure. Based on correlation analysis, results by ABF selection showed a better representation of the lower free tropospheric conditions.

scholarly journals MuSDRI: Multi-Seasonal Decomposition Based Recurrent Imputation for Time Series

2021 ◽  
pp. 1-1
Author(s):  
Yujue Zhou ◽  
Jie Jiang ◽  
Shuang-Hua Yang ◽  
Ligang He ◽  
Yulong Ding
Keyword(s):  
Time Series ◽  
Seasonal Decomposition

scholarly journals Improved Rainfall Prediction Using Combined Pre-Processing Methods and Feed-Forward Neural Networks

J ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 65-83 ◽  
Author(s):  
Duong Tran Anh ◽  
Thanh Duc Dang ◽  
Song Pham Van
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Moving Average ◽  
Monthly Rainfall ◽  
Feed Forward ◽  
Rainfall Prediction ◽  
Feed Forward Neural Networks ◽  
Artificial Neural ◽  
Seasonal Decomposition

Rainfall prediction is a fundamental process in providing inputs for climate impact studies and hydrological process assessments. Rainfall events are, however, a complicated phenomenon and continues to be a challenge in forecasting. This paper introduces novel hybrid models for monthly rainfall prediction in which we combined two pre-processing methods (Seasonal Decomposition and Discrete Wavelet Transform) and two feed-forward neural networks (Artificial Neural Network and Seasonal Artificial Neural Network). In detail, observed monthly rainfall time series at the Ca Mau hydrological station in Vietnam were decomposed by using the two pre-processing data methods applied to five sub-signals at four levels by wavelet analysis, and three sub-sets by seasonal decomposition. After that, the processed data were used to feed the feed-forward Neural Network (ANN) and Seasonal Artificial Neural Network (SANN) rainfall prediction models. For model evaluations, the anticipated models were compared with the traditional Genetic Algorithm and Simulated Annealing algorithm (GA-SA) supported by Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA). Results showed both the wavelet transform and seasonal decomposition methods combined with the SANN model could satisfactorily simulate non-stationary and non-linear time series-related problems such as rainfall prediction, but wavelet transform along with SANN provided the most accurately predicted monthly rainfall.

Seasonal decomposition and forecasting of telecommunication data: A comparative case study

2006 ◽  
Vol 73 (5) ◽  
pp. 495-509 ◽  
Author(s):  
Constantinos S. Hilas ◽  
Sotirios K. Goudos ◽  
John N. Sahalos
Keyword(s):  
Comparative Case Study ◽  
Seasonal Decomposition
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