A Significance Test for Time Series

1942 ◽  
Vol 24 (1) ◽  
pp. 366
Author(s):  
Warren C. Waite ◽  
W. Allen Wallis ◽  
Geoffrey H. Moore
Keyword(s):  
Time Series ◽  
Significance Test

A WAVELET BASED SIGNIFICANCE TEST FOR PERIODICITIES IN INDIAN MONSOON RAINFALL

2008 ◽  
Vol 06 (02) ◽  
pp. 291-304 ◽  
Author(s):  
SARITA AZAD ◽  
R. NARASIMHA ◽  
S. K. SETT
Keyword(s):  
Time Series ◽  
Indian Monsoon ◽  
Statistical Significance ◽  
Significance Test ◽  
Rainfall Time Series ◽  
Reference Spectrum ◽  
Power Spectral ◽  
Reconstructed Time Series ◽  
Fourier Spectral Methods ◽  
Stationary Component

This paper is a sequel to a recent study of the authors' that uses a combination of multiresolution analysis (MRA) and classical Fourier spectral methods, to identify 17 peaks in the power spectral density of the Homogeneous Indian Monsoon (HIM) rainfall time series constructed in Ref. 1. Here we propose a new procedure for testing the statistical significance of these peaks. In this procedure, using MRA the stationary component of the rainfall time series is first identified. Then (partially) reconstructed time series are derived over each scale band in the stationary component. For each of these time series an appropriately colored reference spectrum is derived. The significance of the detected peaks is then determined using a χ2 test against the reference spectra, which together represent a noise process spectrally close to rainfall. It is concluded that HIM rainfall exhibits 10 statistically significant periodicities at a confidence level of 99.9%.

scholarly journals APLIKASI METODE ARIMA BOX-JENKINS UNTUK MERAMALKAN KASUS DBD DI PROVINSI JAWA TIMUR

2019 ◽  
Vol 13 (2) ◽  
pp. 183
Author(s):  
Muhammad Bintang Pamungkas
Keyword(s):  
Time Series ◽  
Significance Test ◽  
Time Series Forecasting ◽  
Forecasting Model ◽  
Upward Trend ◽  
Independent Variables ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Dependent Variables ◽  
Seasonal Data

The Box-Jenkins forecasting method is one of the time series forecasting methods. This method uses past values as dependent variables and independent variables are ignored. Box-Jenkins (ARIMA) method has advantages that can be used on non-stationary data, can be used on all data patterns including seasonal data patterns so this method can be used to predict cases of DHF in East Java Province. This research was conducted to determine the best model with seasonal ARIMA forecasting model and also to analyze the result of DHF case forecasting in East Java Province. The analysis result shows that the best model for DHF case in East Java Province is ARIMA (1,1,2)(2,1,1)12. The best model has fulfilled the test requirement that is parameter significance test and diagnostics check. Forecasting results show the number of DHF cases in 2017-2018 will experience an upward trend. The total number of DHF cases in 2017 was 14,277 cases and increased to 22,284.54 DHF cases in 2018. The forecasting results showed that the highest peak of DHF cases occurred in January 2017 with 1,914.22 cases and then decrease in the next month until the lowest case occurred in October with 768.46. The forecast for 2018 also shows that the highest DHF cases occurred in January with 3455.55 and declined to the lowest in October with 1126.49 cases. MAPE value in the forecast is 43.51%. The MAPE value indicates that the forecasting is good enough, adequate and feasible to use.

A Significance Test for Time Series.

1942 ◽  
Vol 37 (217) ◽  
pp. 152
Author(s):  
P. S. Olmstead ◽  
W. Allen Wallis ◽  
Geoffrey H. Moore
Keyword(s):  
Time Series ◽  
Significance Test

Into the Future

2019 ◽  
pp. 154-170
Author(s):  
David McDowall ◽  
Richard McCleary ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Significance Test ◽  
Undue Influence ◽  
Causal Inferences ◽  
Conceptual Issue ◽  
Bayesian Approaches ◽  
Control Series ◽  
Control Time ◽  
Significance Levels ◽  
Near Future

Chapter 6 introduces two conceptual issues that, in our opinion, will become important in the near future. The first involves the validity of statistical inference. Critics of the conventional null hypothesis significance test generally focus on the undue influence of sample size on p-values and the common misinterpretation of significance levels. Bayesian approaches address and, to some extent, solve both shortcomings. The second conceptual issue involves the use of control time series. As a rule, valid causal inferences require the use of a contrasting control time series. In most instances, no ideal control series is available; however, a synthetic ideal control series can sometimes be constructed from an ensemble of less-than-ideal control time series.

scholarly journals New significance test methods for Fourier analysis of geophysical time series

2011 ◽  
Vol 18 (5) ◽  
pp. 643-652 ◽  
Author(s):  
Z. Zhang ◽  
J. Moore
Keyword(s):  
Time Series ◽  
Significance Test ◽  
Test Methods ◽  
Test Method ◽  
The Arctic ◽  
Significance Level ◽  
Geophysical Time Series ◽  
The Difference ◽  
The Arctic Oscillation ◽  
Intrinsic Feature

Abstract. When one applies the discrete Fourier transform to analyze finite-length time series, discontinuities at the data boundaries will distort its Fourier power spectrum. In this paper, based on a rigid statistics framework, we present a new significance test method which can extract the intrinsic feature of a geophysical time series very well. We show the difference in significance level compared with traditional Fourier tests by analyzing the Arctic Oscillation (AO) and the Nino3.4 time series. In the AO, we find significant peaks at about 2.8, 4.3, and 5.7 yr periods and in Nino3.4 at about 12 yr period in tests against red noise. These peaks are not significant in traditional tests.

scholarly journals Geometric and topological approaches to significance testing in wavelet analysis

2014 ◽  
Vol 1 (2) ◽  
pp. 1331-1363
Author(s):  
J. A. Schulte ◽  
C. Duffy ◽  
R. G. Najjar
Keyword(s):  
Time Series ◽  
North Atlantic ◽  
Topological Analysis ◽  
Significance Test ◽  
Noise Model ◽  
Significance Testing ◽  
Wavelet Domain ◽  
Topological Methods ◽  
Significance Levels ◽  
Wavelet Spectra

Abstract. Geometric and topological methods are applied to significance testing in the wavelet domain. A geometric test was developed for assigning significance to pointwise significance patches in local wavelet spectra, contiguous regions of significant wavelet coefficients with respect to some noise model. This geometric significance test was found to produce results similar to an existing areawise significance test, while being more computationally flexible and efficient. The geometric significance test can be readily applied to pointwise significance patches at various pointwise significance levels in wavelet power and coherence spectra. A topological analysis of pointwise significance patches determined that holes, deficits in pointwise significance embedded in significance patches, are capable of identifying important structures, some of which are undetected by the geometric and areawise tests. The application of the new and existing significance tests to ideal time series and to the time series of the Niño 3.4 and North Atlantic Oscillation showed that the areawise and geometric tests perform similarly in ideal and geophysical settings, while the topological methods showed that the Niño 3.4 time series contains numerous phase-coherent oscillations.

scholarly journals Testing The Order of Markov Dependence in Error Data of IEEE802.11a Standard

2019 ◽  
pp. 617-625
Author(s):  
Shaghayegh Kordnoori ◽  
Hamidreza Mostafaei ◽  
Mohammad Hassan Behzadi
Keyword(s):  
Time Series ◽  
Sample Size ◽  
Markov Property ◽  
Surrogate Data ◽  
Significance Test ◽  
Uniform Sampling ◽  
Markov Dependence

The Markov order is a crucial measure of the memory of a process and its information is essential for appropriate simulation of aspects of the process. In this paper we suggest a robust and straightforward exact significance test based on generating surrogate data to assess the Markov order of a time series. This method is valid for any sample size and certifies a uniform sampling from the set of sequences that definitely have the nth order characteristics of the observed data. The Markov property and order of IEEE802.11a errors are investigated using this test.

scholarly journals Geometric and topological approaches to significance testing in wavelet analysis

2015 ◽  
Vol 22 (2) ◽  
pp. 139-156 ◽  
Author(s):  
J. A. Schulte ◽  
C. Duffy ◽  
R. G. Najjar
Keyword(s):  
Time Series ◽  
Topological Analysis ◽  
Significance Test ◽  
Noise Model ◽  
Significance Testing ◽  
Topological Methods ◽  
Noise Background ◽  
The North ◽  
Significance Levels ◽  
The North Atlantic Oscillation

Abstract. Geometric and topological methods are applied to significance testing in the wavelet domain. A geometric test was developed for assigning significance to pointwise significance patches in local wavelet spectra, i.e., contiguous regions of significant wavelet power coefficients with respect to some noise model. This geometric significance test was found to produce results similar to an existing areawise significance test while being more computationally flexible and efficient. The geometric significance test can be readily applied to pointwise significance patches at various pointwise significance levels in wavelet power and coherence spectra. The geometric test determined that features in wavelet power of the North Atlantic Oscillation (NAO) are indistinguishable from a red-noise background, suggesting that the NAO is a stochastic, unpredictable process, which could render difficult the future projections of the NAO under a changing global system. The geometric test did, however, identify features in the wavelet power spectrum of an El Niño index (Niño 3.4) as distinguishable from a red-noise background. A topological analysis of pointwise significance patches determined that holes, deficits in pointwise significance embedded in significance patches, are capable of identifying important structures, some of which are undetected by the geometric and areawise tests. The application of the topological methods to ideal time series and to the time series of the Niño 3.4 and NAO indices showed that the areawise and geometric tests perform similarly in ideal and geophysical settings, while the topological methods showed that the Niño 3.4 time series contains numerous phase-coherent oscillations that could be interacting nonlinearly.

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