scholarly journals A seasonal fractional ARIMA Model applied to the Nile River monthly flows at Aswan

2000 ◽  
Vol 36 (5) ◽  
pp. 1249-1259 ◽  
Author(s):  
Alberto Montanari ◽  
Renzo Rosso ◽  
Murad S. Taqqu
Keyword(s):  
Arima Model ◽  
Nile River ◽  
Fractional Arima

scholarly journals Trend assessment: applications for hydrology and climate research

2005 ◽  
Vol 12 (2) ◽  
pp. 201-210 ◽  
Author(s):  
M. Kallache ◽  
H. W. Rust ◽  
J. Kropp
Keyword(s):  
Wavelet Analysis ◽  
Large Scale ◽  
Large Time ◽  
Arima Model ◽  
Trend Component ◽  
Deterministic Trend ◽  
Run Off ◽  
Fractional Arima ◽  
Trend Assessment

Abstract. The assessment of trends in climatology and hydrology still is a matter of debate. Capturing typical properties of time series, like trends, is highly relevant for the discussion of potential impacts of global warming or flood occurrences. It provides indicators for the separation of anthropogenic signals and natural forcing factors by distinguishing between deterministic trends and stochastic variability. In this contribution river run-off data from gauges in Southern Germany are analysed regarding their trend behaviour by combining a deterministic trend component and a stochastic model part in a semi-parametric approach. In this way the trade-off between trend and autocorrelation structure can be considered explicitly. A test for a significant trend is introduced via three steps: First, a stochastic fractional ARIMA model, which is able to reproduce short-term as well as long-term correlations, is fitted to the empirical data. In a second step, wavelet analysis is used to separate the variability of small and large time-scales assuming that the trend component is part of the latter. Finally, a comparison of the overall variability to that restricted to small scales results in a test for a trend. The extraction of the large-scale behaviour by wavelet analysis provides a clue concerning the shape of the trend.

Fitting a fractional ARIMA model to time series data

1996 ◽  
Vol 17 (3) ◽  
pp. 467-483 ◽  
Author(s):  
Sat N. Gupta ◽  
Joel Goldstein ◽  
Chang Yu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Arima Model ◽  
Series Data ◽  
Fractional Arima

scholarly journals A long range dependent model with nonlinear innovations for simulating daily river flows

2004 ◽  
Vol 4 (2) ◽  
pp. 277-283 ◽  
Author(s):  
P. Elek ◽  
L. Márkus
Keyword(s):  
Long Memory ◽  
Arima Model ◽  
Smooth Transition ◽  
River Flows ◽  
Discharge Data ◽  
High Quantiles ◽  
Fractional Arima ◽  
Proposed Model ◽  
Significant Difference ◽  
Non Gaussian

Abstract. We present the analysis aimed at the estimation of flood risks of Tisza River in Hungary on the basis of daily river discharge data registered in the last 100 years. The deseasonalised series has skewed and leptokurtic distribution and various methods suggest that it possesses substantial long memory. This motivates the attempt to fit a fractional ARIMA model with non-Gaussian innovations as a first step. Synthetic streamflow series can then be generated from the bootstrapped innovations. However, there remains a significant difference between the empirical and the synthetic density functions as well as the quantiles. This brings attention to the fact that the innovations are not independent, both their squares and absolute values are autocorrelated. Furthermore, the innovations display non-seasonal periods of high and low variances. This behaviour is characteristic to generalised autoregressive conditional heteroscedastic (GARCH) models. However, when innovations are simulated as GARCH processes, the quantiles and extremes of the discharge series are heavily overestimated. Therefore we suggest to fit a smooth transition GARCH-process to the innovations. In a standard GARCH model the dependence of the variance on the lagged innovation is quadratic whereas in our proposed model it is a bounded function. While preserving long memory and eliminating the correlation from both the generating noise and from its square, the new model is superior to the previously mentioned ones in approximating the probability density, the high quantiles and the extremal behaviour of the empirical river flows.

The Effects of Significant International Sports Events on Hungarian Suicide Rates

Crisis ◽  
2016 ◽  
Vol 37 (2) ◽  
pp. 148-154 ◽  
Author(s):  
Karoly Bozsonyi ◽  
Peter Osvath ◽  
Sandor Fekete ◽  
Lajos Bálint
Keyword(s):  
Significant Relationship ◽  
Olympic Games ◽  
Arima Model ◽  
Age Groups ◽  
The Elderly ◽  
Intervention Analysis ◽  
Middle Aged ◽  
Sports Events ◽  
Sport Events ◽  
The Olympic Games

Abstract. Background: Several studies found a significant relationship between important sport events and suicidal behavior. Aims: We set out to investigate whether there is a significant relationship between the raw suicide rate and the most important international sports events (Olympic Games, FIFA World Cup, UEFA European Championship) in such an achievement-oriented society as the Hungarian one, where these sport events receive great attention. Method: We examined suicide cases occurring over 15,706 days between January 1, 1970, and December 31, 2012 (43 years), separately for each gender. Because of the age-specific characteristics of suicide, the effects of these sport events were analyzed for the middle-aged (30–59 years old) and the elderly (over 60 years old) generations as well as for gender-specific population groups. The role of international sport events was examined with the help of time-series intervention analysis after cyclical and seasonal components were removed. Intervention analysis was based on the ARIMA model. Results: Our results showed that only the Olympic Games had a significant effect in the middle-aged population. Neither in the older male nor in any of the female age groups was a relationship between suicide and Olympic Games detected. Conclusion: The Olympic Games seem to decrease the rate of suicide among middle-aged men, slightly but significantly.

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