Over-whitening or Pre-whitening in Hydro-climatological Trend Analysis?

To meet the basic assumption of classical Mann-Kendall (MK) trend analysis, which requires serially independent time series, a pre-whitening (PW) procedure is proposed to alleviate the serial correlation structure of a given hydro-meteorological time series records for application. The procedure is simply to take the lagged differences in a given time series in the hope that the new time series will have an independent serial correlation coefficient. The whole idea was originally based on the first-order autoregressive AR (1) process, but such a procedure has been documented to damage the trend component in the original time series. On the other hand, the over-whitening procedure (OW) proposes a white noise process superposition of the same length with zero mean and some standard deviation on the original time series to convert it into serially independent series without any damage to the trend component. The stationary white noise addition does not have any trend components. For trend identification, annual average temperature records in New Jersey and Istanbul are presented to show the difference between PW and OW procedures. It turned out that the OW procedure was superior to the PW procedure, which did not cause a loss in the original trend component.

Exploring BIS credit-to-GDP gap critiques: the Swiss case

A growing body of literature has highlighted two important caveats to the credit-to-GDP gap as advocated by the Bank for International Settlements (BIS). The first relates to the approach used to normalise credit (i.e. dividing nominal credit by GDP). In this regard, critics have argued that GDP movements, that may or may not be relevant, run the risk of affecting a normalised measure of credit. The second relates to the use of the Hodrick-Prescott (HP) filter to estimate the gap’s trend component. In this regard, critics have emphasised several measurement problems associated with using the HP filter. In this paper, we assess the relevance of these critiques for Switzerland. Our findings show that despite its drawbacks, the BIS gap is a reliable measure of excess credit in Switzerland. Alternatives do not provide clear advantages, rather they are considerably more complex to estimate and come with their own set of pitfalls. For policymaking purposes, the BIS gap’s signal should be complemented with narratives based on a broader set of credit metrics to ensure that an all-encompassing risk assessment is made.

Trend, Seasonal, and Irregular Variations in Regional Actual Evapotranspiration Over China: A Multi-Dataset Analysis

Actual evapotranspiration (AE) is a crucial processes in terrestrial ecosystems. Global warming is expected to increase AE; however, various AE estimation methods or models give inconsistent trends. This study analyzed AE variability in China during 1982–2015 based on the Budyko framework (AE_Budyko), a complementary-relationship-based product (AE_CR), and the weighted average of six reanalyses (AE_WAR). Because the response of AE to driving factors and the performances of AE datasets are both scale-dependent, China has been categorized into six distinct climatic areas. From a regional perspective, the X-12-ARIMA method was used to decompose monthly AE into the trend, seasonal, and irregular components. We examined the main characteristics of these components and the relationships of climate factors with AE. The results indicate that the trend component of AE increased from the mid-1990s to the early 2000s and more recently in the hyper-arid and arid areas. Increasing AE was observed from 1982 to the early 1990s in the semi-arid and dry sub-humid areas. AE increased significantly and had substantial interannual variability for the entire period in the sub-humid and humid areas. Increased precipitation and water supply from terrestrial water storage contributed significantly to increasing AE in the drylands. The simultaneous occurrence of increasing precipitation and wet-day frequency caused increasing AE in the dry sub-humid area. Increased AE could be explained by the increased energy supply and precipitation in the sub-humid and humid areas. Precipitation had the strongest influence on the irregular component of AE in drylands. AE and potential evapotranspiration had a strong positive correlation in the sub-humid and humid areas. Regarding data availability, a discrepancy existed in the trend component of AE_CR because soil moisture was not explicitly considered, whereas the irregular component of AE_Budyko contained distinct variations in humid and sub-humid areas.

Development of a Hybrid Method to Predict the Slope Surface Deformation Utilizing the Time Series and GA-Elman Models

A reliable prediction of the surface deformation of slopes is vital to better assess the fatalities and economic losses caused by landslides. Many prediction methods have been proposed to estimate the surface deformation of landslides with nonlinear characteristics. However, these methods have low accuracy and poor applicability. In this paper, a new hybrid method for surface deformation prediction was proposed, which was deduced from the Wavelet Analysis, Genetic Algorithm (GA), and Elman Algorithm. In this method, the slope surface deformation was decomposed into a trend component and a periodic component using the time series model, which were trained and predicted utilizing the GA-Elman model. The predicted slope surface deformation was the combination of the trend component and the periodic component. Then, the predicted results of slope surface deformation through GA-Elman were compared with the predicted results through Support Vector Machines (SVM), Extreme Learning Machine (ELM), Back Propagation (BP), and Genetic Algorithm-Back Propagation (GA-BP) models. The comparison was made with reference to the data retrieved from the on-site slopes and the laboratory tests. The results revealed that the proposed method highlighted reliability and could be used with higher accuracy to forecast the slope surface deformation in the process of landslides.

Ramadan Effect: A Structural Time-Series Test

This study investigates whether religious belief creates stock market return seasonality, focusing on the Muslim holy month “Ramadan". We use long-term data from 12 stock markets in countries with a high Muslim majority. Using a structural time-series model that takes into account a “trend component" and a stochastic “seasonal component”, we find no significant evidence of Ramadan return seasonality for the 12 stock markets over the long-term. This result suggests that there is no trend component for Ramadan effect and that Ramadan returns seasonality vanish in the long-term.

SELECTION OF METRIC AND CATEGORICAL ATTRIBUTES OF RARE ANOMALOUS EVENTS IN A COMPUTER SYSTEM USING DATA MINING METHODS

The process of marking multi-attribute experimental data for subsequent use by means of data mining in problems of detection and classification of rare anomalous events of computer systems (CS) is considered. The labeling process is carried out using three methods: manual preprocessing, statistical analysis and cluster analysis. Among the attributes of the metric type, the authors identified two macrogroups: “integral attributes” and “impulse attributes”. It is shown that the combination of statistical and cluster analysis methods increases the accuracy of detecting anomalous events in the CS, and also allows the selection of attributes according to their information significance. The expediency of manual preprocessing of data before clustering is shown by the example of dividing attributes into macrogroups, analyzing the density distribution using violin plot and removing the trend component using the method difference stationary series. With the help of construction of violin diagrams (Violin plot) for the attribute of the “integral” macrogroup, the distribution of states of the CS is shown. It is shown that the removal of the trend component by the DS-series method, normalization and reduction to absolute values allows more accurate marking of anomalous outliers, but this is not always acceptable. The interpretation of the clustering results performed for each normalized attribute shows that the normal values for all attributes are concentrated around zero values. The result of labeling experimental data is attribute-labeled data, where each attribute at the current time is assigned one of two states: abnormal or normal.

BASIC STUDY ON A PSEUDO TREND PHENOMENON OF THE FEYNMAN-α ANALYSIS WITH BUNCHING METHOD

In these years, reactor noise analysis methods have been studied to apply for the Debris’ criticality management at the Fukushima Daiichi NPP, Japan. The Feynman-α analysis with bunching method is one of the candidate techniques, however the bunching method itself has never been validated in detail. This synthesis technique is useful to reduce a time required for the experiment, however it is known that a non-physical trend unrelated to the state of a nuclear reactor is generated by the multiple use of time series data, and this phenomenon (we call “pseudo trend phenomenon”) has never been systematically studied in detail. In this study, Poisson events, whose statistical characteristics were clarified, were employed to investigate the pseudo trend phenomenon of the bunching method. The time-sequence count data for various statistical parameters were generated by the Monte Carlo time series simulator. Comparing the two results obtained by applying the conventional bunching method and the moving-bunching method for the same Poisson event time series, and it was found that the same pseudo trend component appears in both results of the bunching method and the moving bunching method. In addition, it was also found that the fluctuation width of the pseudo trend component is smaller than the statistical fluctuation range.