scholarly journals Multiplicative SARIMA models

2003 ◽  
pp. 225-254 ◽  
Author(s):  
Rong Chen ◽  
Rainer Schulz ◽  
Sabine Stephan
Keyword(s):  
Sarima Models

scholarly journals SARIMA Approach to Generating Synthetic Monthly Rainfall in the Sinú River Watershed in Colombia

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 602
Author(s):  
Luisa Martínez-Acosta ◽  
Juan Pablo Medrano-Barboza ◽  
Álvaro López-Ramos ◽  
John Freddy Remolina López ◽  
Álvaro Alberto López-Lambraño
Keyword(s):  
Time Series ◽  
Water Resources ◽  
Autocorrelation Function ◽  
Information Criterion ◽  
Monthly Rainfall ◽  
Statistical Characteristics ◽  
Rainfall Time Series ◽  
River Watershed ◽  
Statistical Measures ◽  
Sarima Models

Seasonal Auto Regressive Integrative Moving Average models (SARIMA) were developed for monthly rainfall time series. Normality of the rainfall time series was achieved by using the Box Cox transformation. The best SARIMA models were selected based on their autocorrelation function (ACF), partial autocorrelation function (PACF), and the minimum values of the Akaike Information Criterion (AIC). The result of the Ljung–Box statistical test shows the randomness and homogeneity of each model residuals. The performance and validation of the SARIMA models were evaluated based on various statistical measures, among these, the Student’s t-test. It is possible to obtain synthetic records that preserve the statistical characteristics of the historical record through the SARIMA models. Finally, the results obtained can be applied to various hydrological and water resources management studies. This will certainly assist policy and decision-makers to establish strategies, priorities, and the proper use of water resources in the Sinú river watershed.

scholarly journals Parametric Methodologies for Detecting Changes in Maximum Temperature of Tlaxco, Tlaxcala, México

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Silvia Herrera Cortés ◽  
Bulmaro Juárez Hernández ◽  
Victor Hugo Vázquez Guevara ◽  
Hugo Adán Cruz Suárez
Keyword(s):  
Linear Time ◽  
Score Test ◽  
Change Points ◽  
Quality Analysis ◽  
Maximum Temperature ◽  
Ratio Test ◽  
Comparison Results ◽  
Binary Segmentation ◽  
Temperature Records ◽  
Sarima Models

In this paper, comparison results of parametric methodologies of change points, applied to maximum temperature records from the municipality of Tlaxco, Tlaxcala, México, are presented. Methodologies considered are likelihood ratio test, score test, and binary segmentation (BS), pruned exact linear time (PELT), and segment neighborhood (SN). In order to compare such methodologies, a quality analysis of the data was performed; in addition, lost data were estimated with linear regression, and finally, SARIMA models were adjusted.

Evolving SARIMA Models Using cGA for Time Series Forecasting

2019 ◽  
Author(s):  
Juan J. Flores ◽  
Josue D. Gonzalez ◽  
Baldwin Cortes ◽  
Cristina Reyes ◽  
Felix Calderon
Keyword(s):  
Time Series ◽  
Time Series Forecasting ◽  
Sarima Models

Tourism forecasting using SARIMA models in Chilean regions

2011 ◽  
Vol 2 (2) ◽  
pp. 176 ◽  
Author(s):  
Juan Gabriel Brida ◽  
Nicolas Garrido
Keyword(s):  
Sarima Models

scholarly journals Comparison between the Holt-Winters and SARIMA Models in the Prediction of NDVI in an Arid Region in Kenya using Pixel-wise NDVI Time Series

2021 ◽  
Vol 2 (23) ◽  
pp. 1-15
Author(s):  
Mwana Said Omar ◽  
◽  
Hajime Kawamukai
Keyword(s):  
Time Series ◽  
Arid Region ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Moving Average ◽  
Additive Model ◽  
Grid Search ◽  
Sarima Model ◽  
Ndvi Time Series ◽  
Sarima Models

Desertification is major issue in arid and semi-arid lands (ASAL) with devastating environmental and socio-economic impacts. Time series analysis was applied on 19 years’ pixel-wise monthly mean Normalized Difference Vegetation Index (NDVI) data. The aim of this study was to identify a time series model that can be used to predict NDVI at the pixel level in an arid region in Kenya. The Holt-Winters and Seasonal Auto Regressive Integrated Moving Average (SARIMA) models were developed and statistical analysis was carried out using both models on the study area. We performed a grid search to optimise and determine the best hyper parameters for the models. Results from the grid search identified the Holt-Winters model as an additive model and a SARIMA model with a trend autoregressive (AR) order of 1, a trend moving average (MA) order of 1 and a seasonal MA order of 2, with both models having a seasonal period of 12 months. It was concluded that the Holt-Winters model showed the best performance for 600 ✕ 600 pixels (MAE = 0.0744, RMSE = 0.096) compared to the SARIMA model.

scholarly journals Forecasting wholesale price of coarse rice in Bangladesh: A seasonal autoregressive integrated moving average approach

2014 ◽  
Vol 11 (2) ◽  
pp. 271-276
Author(s):  
MF Hassan ◽  
MA Islam ◽  
MF Imam ◽  
SM Sayem
Keyword(s):  
Moving Average ◽  
Wholesale Price ◽  
Information Criteria ◽  
Suitable Model ◽  
Monthly Data ◽  
Autoregressive Integrated Moving Average ◽  
Lag Times ◽  
Predicted Values ◽  
Absolute Percent Error ◽  
Sarima Models

This article attempts to develop the model and to forecast the wholesale price of coarse rice in Bangladesh. Seasonal Autoregressive Integrated Moving Average (SARIMA) models have been developed on the monthly data collected from July 1975 to December 2011and validated using the data from December 2010 to December 2011. The results showed that the predicted values were consistent with the upturns and downturns of the observed series. The model with non seasonal autoregressive 1, difference 1 and moving average 1 and seasonal difference 1 and moving average 1 that is SARIMA (1,1,1)(0,1,1)12 model has been found as the most suitable model with least Root Mean Square Error (RMSE) of 61.657, Normalised Bayesian Information Criteria (BIC) of 8.300 and Mean Absolute Percent Error (MAPE) of 3.906. The model was further validated by Ljung-Box test (Q18=17.394 and p>.20) with no significant autocorrelation between residuals at different lag times. Finally, a forecast for the period January 2012 to December 2013 was made. DOI: http://dx.doi.org/10.3329/jbau.v11i2.19925 J. Bangladesh Agril. Univ. 11(2): 271-276, 2013

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