scholarly journals Time series analysis on precipitation with missing data using stochastic SARIMA

MAUSAM ◽  
2021 ◽  
Vol 71 (4) ◽  
pp. 617-624
Author(s):  
SHARMA M. K. ◽  
OMER MOHAMMED ◽  
KIANI SARA
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Time Series Analysis ◽  
Stochastic Model ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Missing Observations ◽  
Full Data ◽  
Sarima Model ◽  
Monthly Precipitation Data

This paper presents an application of the Box-Jenkins methodology for modeling the precipitation in Iran. Linear stochastic model known as multiplicative seasonal ARIMA was used to model the monthly precipitation data for 44 years. Missing data occurred in between for 34 months for some reason. To fill the gap a SARIMA model was fitted based on the first 180 available observations and the missing observations were substituted by the forecasts for the next 34 months. Then a SARIMA model was fitted for the full data. The result showed that the fitted model represent the full data well.

scholarly journals Assessment of different methods for estimation of missing data in precipitation studies

2016 ◽  
Vol 48 (4) ◽  
pp. 1032-1044 ◽  
Author(s):  
Mohammad-Taghi Sattari ◽  
Ali Rezazadeh-Joudi ◽  
Andrew Kusiak
Keyword(s):  
Missing Data ◽  
Missing Values ◽  
Regression Method ◽  
Linear Regression Method ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Multiple Regression Method ◽  
Multiple Imputation Method ◽  
Estimation Of Missing Data ◽  
Monthly Precipitation Data

The outcome of data analysis depends on the quality and completeness of data. This paper considers various techniques for filling in missing precipitation data. To assess suitability of the different methods for filling in missing data, monthly precipitation data collected at six different stations was considered. The complete sets (with no missing values) are used to predict monthly precipitation. The arithmetic averaging method, the multiple linear regression method, and the non-linear iterative partial least squares algorithm perform best. The multiple regression method provided a successful estimation of the missing precipitation data, which is supported by the results published in the literature. The multiple imputation method produced the most accurate results for precipitation data from five dependent stations. The decision-tree algorithm is explicit, and therefore it is used when insights into the decision making are needed. Comprehensive error analysis is presented.

A Method Based on MTLS and ILSP for GNSS Coordinate Time Series Analysis with Missing Data

2021 ◽  
Author(s):  
Yingying Ren ◽  
Hu Wang ◽  
Lizhen Lian ◽  
Jiexian Wang ◽  
Yingyan Cheng ◽  
...  
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Time Series Analysis ◽  
Coordinate Time ◽  
Coordinate Time Series ◽  
Series Analysis

AN EVOLUTIONARY APPROACH FOR IMPUTING MISSING DATA IN TIME SERIES

2010 ◽  
Vol 19 (01) ◽  
pp. 107-121 ◽  
Author(s):  
JUAN CARLOS FIGUEROA GARCÍA ◽  
DUSKO KALENATIC ◽  
CESAR AMILCAR LÓPEZ BELLO
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Missing Data ◽  
Genetic Structure ◽  
Evolutionary Algorithm ◽  
Fitness Function ◽  
Error Function ◽  
Missing Observations ◽  
Mean And Variance ◽  
Methodological Aspects

This paper presents a proposal based on an evolutionary algorithm for imputing missing observations in time series. A genetic algorithm based on the minimization of an error function derived from their autocorrelation function, mean, and variance is presented. All methodological aspects of the genetic structure are presented. An extended description of the design of the fitness function is provided. Four application examples are provided and solved by using the proposed method.

scholarly journals Analysis of total monthly precipitation of Susurluk Basin in Turkey using innovative polygon trend analysis method

2020 ◽  
Author(s):  
Gokmen Ceribasi ◽  
Ahmet Iyad Ceyhunlu
Keyword(s):  
Climate Change ◽  
Trend Analysis ◽  
Meteorological Parameters ◽  
Global Climate ◽  
Data Series ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Analysis Method ◽  
Distinctive Features ◽  
Monthly Precipitation Data

Abstract The effects of climate change caused by global warming can be seen in changes of climate variables such as precipitation, humidity, and temperatures. These effects of global climate change can be interpreted as a result of the examination of meteorological parameters. One of the most effective methods to investigate these effects is trend analysis. The Innovative Polygon Trend Analysis (IPTA) method is a trend analysis method that has emerged in recent years. The distinctive features of this method compared with other trend methods are that it depends on time series and can compare data series among themselves. Therefore, in this study, the IPTA method was applied to total monthly precipitation data of Susurluk Basin, one of Turkey's important basins. Data from ten precipitation observation stations in Susurluk Basin were used. Data were provided by the General Directorate of State Meteorology Affairs. The length of this data series was 12 years (2006–2017). As a result of the study, since there is no regular polygon in IPTA graphics of each station, it is seen that precipitation data varies by years. While this change is seen increasingly at some stations, it is seen decreasingly at other stations.

scholarly journals Global and regional performances of SPI candidate distribution functions in observations and simulations

2020 ◽  
Author(s):  
Patrick Pieper ◽  
André Düsterhus ◽  
Johanna Baehr
Keyword(s):  
Time Series ◽  
Distribution Function ◽  
Weibull Distribution ◽  
Standardized Precipitation Index ◽  
Distribution Functions ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Data Set ◽  
Exponentiated Weibull Distribution ◽  
Exponentiated Weibull

Abstract. The Standardized Precipitation Index (SPI) is a widely accepted drought index. Its calculation algorithm normalizes the index via a distribution function. Which distribution function to use is still disputed within literature. This study illuminates the long-standing dispute and proposes a solution which ensures the normality of the index for all common accumulation periods in observations and simulations. We compare the normality of SPI time-series derived with the gamma, Weibull, generalized gamma, and the exponentiated Weibull distribution. Our normality comparison evaluates actual against theoretical occurrence probabilities of SPI categories, and the quality of the fit of candidate distribution functions against their complexity with Akaike's Information Criterion. SPI time-series, spanning 1983–2013, are calculated from Global Precipitation Climatology Project's monthly precipitation data-set and seasonal precipitation hindcasts from the Max Planck Institute Earth System Model. We evaluate these SPI time-series over the global land area and for each continent individually during winter and summer. While focusing on an accumulation period of 3-months, we additionally test the drawn conclusions for other common accumulation periods (1-, 6-, 9-, and 12-months). Our results suggest to exercise caution when using the gamma distribution to calculate SPI; especially in simulations or their evaluation. Further, our analysis shows a distinctly improved normality for SPI time-series derived with the exponentiated Weibull distribution relative to other distributions. The use of the exponentiated Weibull distribution maximizes the normality of SPI time-series in observations and simulations both individual as well as concurrent. Its use further maximizes the normality of SPI time-series over each continent and for every investigated accumulation period. We, therefore, advocate to derive SPI with the exponentiated Weibull distribution, irrespective of the heritage of the precipitation data or the length of analyzed accumulation periods.

scholarly journals Time Scale Analysis of Precipitation Based on EMD Method in Xilin Gol

2019 ◽  
Vol 136 ◽  
pp. 05004
Author(s):  
Wenjing Suo ◽  
Guodong Wu ◽  
Heru Xue
Keyword(s):  
Time Series ◽  
Animal Husbandry ◽  
Monthly Precipitation ◽  
Practical Application ◽  
Contribution Rate ◽  
North Central ◽  
The North ◽  
Important Practical Application ◽  
Monthly Precipitation Data ◽  
Emd Method

Xilin Gol is located in the north-central part of Inner Mongolia, and its western plains are extremely scarce areas of water resources.The analysis of the precipitation in Xilin Gol is of great significance to the economic development of local animal husbandry. Time series analysis and prediction have important practical application value, and scientific and accurate prediction results can provide a key guiding role for social activities. Combined with the fast Fourier transform, the empirical modal decomposition method was used to analyze the monthly precipitation data of Xilin Gol from 1961 to 2016.The results show that the time series has a period of 6 and about 22 months.And through the calculation of the contribution rate of the IMF variance, it is found that 12 months is the main cycle of the monthly precipitation of Xilin Gol,The results show that EMD can more accurately reflect the periodic characteristics of precipitation. The research results have certain practical value for the scientific and rational use of precipitation.

scholarly journals Best-fit probability distribution models for monthly rainfall of Northeastern Brazil

2021 ◽  
Author(s):  
Patricia de Souza Medeiros Pina Ximenes ◽  
Antonio Samuel Alves da Silva ◽  
Fahim Ashkar ◽  
Tatijana Stosic
Keyword(s):  
Northeastern Brazil ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Distribution Models ◽  
Drought Period ◽  
Natural Systems ◽  
Generalized Pareto ◽  
Log Normal ◽  
Monthly Precipitation Data ◽  
Parameter Distributions

Abstract The analysis of precipitation data is extremely important for strategic planning and decision-making in various natural systems, as well as in planning and preparing for a drought period. The drought is responsible for several impacts on the economy of Northeast Brazil (NEB), mainly in the agricultural and livestock sectors. This study analyzed the fit of 2-parameter distributions gamma (GAM), log-normal (LNORM), Weibull (WEI), generalized Pareto (GP), Gumbel (GUM) and normal (NORM) to monthly precipitation data from 293 rainfall stations across NEB, in the period 1988–2017. The maximum likelihood (ML) method was used to estimate the parameters to fit the models and the selection of the model was based on a modification of the Shapiro-Wilk statistic. The results showed the chosen 2-parameter distributions to be flexible enough to describe the studied monthly precipitation data. The GAM and WEI models showed the overall best fits, but the LNORM and GP models gave the best fits in certain months of the year and regions that differed from the others in terms of their average precipitation.

scholarly journals Forecasting the Electric Energy Supply in Duhok Province using Proposed Methods Based on Wavelet Analysis and Sarima Methods

2017 ◽  
Vol 5 (2) ◽  
pp. 221
Author(s):  
Qais Mustafa Abdulqader
Keyword(s):  
Time Series ◽  
Wavelet Analysis ◽  
Time Series Analysis ◽  
Sample Size ◽  
Energy Supply ◽  
Electric Energy ◽  
Real Data ◽  
Time Series Forecasting ◽  
Sarima Model ◽  
Quarterly Data

Many applications have been done in the field of using wavelet analysis for time series analysis. In this study, we used the quarterly data of Electric Energy Supply in Duhok Province-Iraq in Megawatt which represents a sample size (46) observations during the period 2004 and 2015.we aim to describe how wavelet de-noising can be used in time series forecasting and improve the forecasting quality through presenting some proposed methods based on wavelet analysis and SARIMA method and applying on real data and make comparison between methods depending on some statistical criteria.Results from the analysis showed the superiority of the three proposed methods and showed that we can get more information from a series when using Wavelet-SARIMA method and this leads to enhance the classical SARIMA model in forecasting. Furthermore, after many empirical experiments with many wavelet families, it has been found that Daubechies, Coiflets, Discrete Meyer(dmey) and Symlet wavelets are very suitable when denoising the data and out of these four wavelet families, the Daubechies and Discrete Meyer performed better.

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