Analysis of financial data series using fractional Fourier transform and multidimensional scaling

The goal of this study is the analysis of the dynamical properties of financial data series from worldwide stock market indexes during the period 2000–2009. We analyze, under a regional criterium, ten main indexes at a daily time horizon. The methods and algorithms that have been explored for the description of dynamical phenomena become an effective background in the analysis of economical data. We start by applying the classical concepts of signal analysis, fractional Fourier transform, and methods of fractional calculus. In a second phase we adopt the multidimensional scaling approach. Stock market indexes are examples of complex interacting systems for which a huge amount of data exists. Therefore, these indexes, viewed from a different perspectives, lead to new classification patterns.

Integer and Fractional Order Entropy Analysis of Earthquake Data-series

This paper studies the statistical distributions of worldwide earthquakes from year 1963 up to year 2012. A Cartesian grid, dividing Earth into geographic regions, is considered. Entropy and the Jensen–Shannon divergence are used to analyze and compare real-world data. Hierarchical clustering and multi-dimensional scaling techniques are adopted for data visualization. Entropy-based indices have the advantage of leading to a single parameter expressing the relationships between the seismic data. Classical and generalized (fractional) entropy and Jensen–Shannon divergence are tested. The generalized measures lead to a clear identification of patterns embedded in the data and contribute to better understand earthquake distributions.

Homogenization of Portuguese long-term temperature data series: Lisbon, Coimbra and Porto

Three long-term temperature data series measured in Portugal were studied to detect and correct non-climatic homogeneity breaks and are now available for future studies of climate variability. Series of monthly minimum (Tmin) and maximum (Tmax) temperatures measured in the three Portuguese meteorological stations of Lisbon (from 1856 to 2008), Coimbra (from 1865 to 2005) and Porto (from 1888 to 2001) were studied to detect and correct non-climatic homogeneity breaks. These series together with monthly series of average temperature (Taver) and temperature range (DTR) derived from them were tested in order to detect homogeneity breaks, using, firstly, metadata, secondly, a visual analysis and, thirdly, four widely used homogeneity tests: von Neumann ratio test, Buishand test, standard normal homogeneity test and Pettitt test. The homogeneity tests were used in absolute (using temperature series themselves) and relative (using sea-surface temperature anomalies series obtained from HadISST2 close to the Portuguese coast or already corrected temperature series as reference series) modes. We considered the Tmin, Tmax and DTR series as most informative for the detection of homogeneity breaks due to the fact that Tmin and Tmax could respond differently to changes in position of a thermometer or other changes in the instrument's environment; Taver series have been used, mainly, as control. The homogeneity tests show strong inhomogeneity of the original data series, which could have both internal climatic and non-climatic origins. Homogeneity breaks which have been identified by the last three mentioned homogeneity tests were compared with available metadata containing data, such as instrument changes, changes in station location and environment, observing procedures, etc. Significant homogeneity breaks (significance 95% or more) that coincide with known dates of instrumental changes have been corrected using standard procedures. It was also noted that some significant homogeneity breaks, which could not be connected to the known dates of any changes in the park of instruments or stations location and environment, could be caused by large volcanic eruptions. The corrected series were again tested for homogeneity: the corrected series were considered free of non-climatic breaks when the tests of most of monthly series showed no significant (significance 95% or more) homogeneity breaks that coincide with dates of known instrument changes. Corrected series are now available in the frame of ERA-CLIM FP7 project for future studies of climate variability.

Detection and mapping of burnt areas from time series of MODIS-derived NDVI data in a Mediterranean region

Moderate resolution remote sensing data, as provided by MODIS, can be used to detect and map active or past wildfires from daily records of suitable combinations of reflectance bands. The objective of the present work was to develop and test simple algorithms and variations for automatic or semiautomatic detection of burnt areas from time series data of MODIS biweekly vegetation indices for a Mediterranean region. MODIS-derived NDVI 250m time series data for the Valencia region, East Spain, were subjected to a two-step process for the detection of candidate burnt areas, and the results compared with available fire event records from the Valencia Regional Government. For each pixel and date in the data series, a model was fitted to both the previous and posterior time series data. Combining drops between two consecutive points and 1-year average drops, we used discrepancies or jumps between the pre and post models to identify seed pixels, and then delimitated fire scars for each potential wildfire using an extension algorithm from the seed pixels. The resulting maps of the detected burnt areas showed a very good agreement with the perimeters registered in the database of fire records used as reference. Overall accuracies and indices of agreement were very high, and omission and commission errors were similar or lower than in previous studies that used automatic or semiautomatic fire scar detection based on remote sensing. This supports the effectiveness of the method for detecting and mapping burnt areas in the Mediterranean region.

Data driven modeling for power transformer lifespan evaluation

Large sized power transformers are important parts of the power supply chain. These very critical networks of engineering assets are an essential base of a nation’s energy resource infrastructure. This research identifies the key factors influencing transformer normal operating conditions and predicts the asset management lifespan. Engineering asset research has developed few lifespan forecasting methods combining real-time monitoring solutions for transformer maintenance and replacement. Utilizing the rich data source from a remote terminal unit (RTU) system for sensor-data driven analysis, this research develops an innovative real-time lifespan forecasting approach applying logistic regression based on the Weibull distribution. The methodology and the implementation prototype are verified using a data series from 161 kV transformers to evaluate the efficiency and accuracy for energy sector applications. The asset stakeholders and suppliers significantly benefit from the real-time power transformer lifespan evaluation for maintenance and replacement decision support.

System and Method for mining of Temporal Data

A system for temporal data mining includes a computer readable medium having an application configured to receive at an input module a temporal data series having events with start times and end times, a set of allowed dwelling times and a threshold frequency. The system is further configured to identify, using a candidate identification and tracking module, one or more occurrences in the temporal data series of a candidate episode and increment a count for each identified occurrence. The system is also configured to produce at an output module an output for those episodes whose count of occurrences results in a frequency exceeding the threshold frequency.

Dynamical structure in paleoclimate data

Deterministic chaos in dynamical systems offers a new paradigm for understanding irregular fluctuations. The theory of chaotic dynamical systems includes methods that can test whether any given set of time series data, such as paleoclimate proxy data, are consistent with a deterministic interpretation. Paleoclimate data with annual resolution and absolute dating provide multiple channels of concurrent time series; these multiple time series can be treated as potential phase space coordinates to test whether interannual climate variability is deterministic. Dynamical structure tests which take advantage of such multichannel data are proposed and illustrated by application to a simple synthetic model of chaos, and to two paleoclimate proxy data series.

The evaluation of time series: their scientific value and contribution to policy needs. Occasional Publication of the Marine Biological Association 22

In 2000 a Review of Current Marine Observations in relation to present and future needs was undertaken by the Inter-Agency Committee for Marine Science and Technology (IACMST). The Marine Environmental Change Network (MECN) was initiated in 2002 as a direct response to the recommendations of the report. A key part of the current phase of the MECN is to ensure that information from the network is provided to policy makers and other end-users to enable them to produce more accurate assessments of ecosystem state and gain a clearer understanding of factors influencing change in marine ecosystems. The MECN holds workshops on an annual basis, bringing together partners maintaining time-series and long-term datasets as well as end-users interested in outputs from the network. It was decided that the first workshop of the MECN continuation phase should consist of an evaluation of the time series and data sets maintained by partners in the MECN with regard to their ‘fit for purpose’ for answering key science questions and informing policy development. This report is based on the outcomes of the workshop. Section one of the report contains a brief introduction to monitoring, time series and long-term datasets. The various terms are defined and the need for MECN type data to complement compliance monitoring programmes is discussed. Outlines are also given of initiatives such as the United Kingdom Marine Monitoring and Assessment Strategy (UKMMAS) and Oceans 2025. Section two contains detailed information for each of the MECN time series / long-term datasets including information on scientific outputs and current objectives. This information is mainly based on the presentations given at the workshop and therefore follows a format whereby the following headings are addressed: Origin of time series including original objectives; current objectives; policy relevance; products (advice, publications, science and society). Section three consists of comments made by the review panel concerning all the time series and the network. Needs or issues highlighted by the panel with regard to the future of long-term datasets and time-series in the UK are shown along with advice and potential solutions where offered. The recommendations are divided into 4 categories; ‘The MECN and end-user requirements’; ‘Procedures & protocols’; ‘Securing data series’ and ‘Future developments’. Ever since marine environmental protection issues really came to the fore in the 1960s, it has been recognised that there is a requirement for a suitable evidence base on environmental change in order to support policy and management for UK waters. Section four gives a brief summary of the development of marine policy in the UK along with comments on the availability and necessity of long-term marine observations for the implementation of this policy. Policy relating to three main areas is discussed; Marine Conservation (protecting biodiversity and marine ecosystems); Marine Pollution and Fisheries. The conclusion of this section is that there has always been a specific requirement for information on long-term change in marine ecosystems around the UK in order to address concerns over pollution, fishing and general conservation. It is now imperative that this need is addressed in order for the UK to be able to fulfil its policy commitments and manage marine ecosystems in the light of climate change and other factors.

Development of a novel continuous statistical modelling technique for detecting the adulteration of extra virgin olive oil with hazelnut oil by using spectroscopic data

The adulteration of extra virgin olive oil with other vegetable oils is a certain problem with economic and health consequences. Current official methods have been proved insufficient to detect such adulterations. One of the most concerning and undetectable adulterations with other vegetable oils is the addition of hazelnut oil. The main objective of this work was to develop a novel dimensionality reduction technique able to model oil mixtures as a part of an integrated pattern recognition solution. This final solution attempts to identify hazelnut oil adulterants in extra virgin olive oil at low percentages based on spectroscopic chemical fingerprints. The proposed Continuous Locality Preserving Projections (CLPP) technique allows the modelling of the continuous nature of the produced in house admixtures as data series instead of discrete points. This methodology has potential to be extended to other mixtures and adulterations of food products. The maintenance of the continuous structure of the data manifold lets the better visualization of this examined classification problem and facilitates a more accurate utilisation of the manifold for detecting the adulterants.

Optimal alarms systems and its application to financial time series

This thesis focuses on the application of optimal alarm systems to non linear time series models. The most common classes of models in the analysis of real-valued and integer-valued time series are described. The construction of optimal alarm systems is covered and its applications explored. Considering models with conditional heteroscedasticity, particular attention is given to the Fractionally Integrated Asymmetric Power ARCH, FIAPARCH(p; d; q) model and an optimal alarm system is implemented, following both classical and Bayesian methodologies. Taking into consideration the particular characteristics of the APARCH(p; q) representation for financial time series, the introduction of a possible counterpart for modelling time series of counts is proposed: the INteger-valued Asymmetric Power ARCH, INAPARCH(p; q). The probabilistic properties of the INAPARCH(1; 1) model are comprehensively studied, the conditional maximum likelihood (ML) estimation method is applied and the asymptotic properties of the conditional ML estimator are obtained. The final part of the work consists on the implementation of an optimal alarm system to the INAPARCH(1; 1) model. An application is presented to real data series.

Blind equalisation of multilevel PAM data for nonminimum phase channels via second- and fourth-order cumulants

This paper introduces a new blind equalisation algorithm for the pulse amplitude modulation (PAM) data transmitted through nonminimum phase (NMP) channels. The algorithm itself is based on a noncausal AR model of communication channels and the second- and fourth-order cumulants of the received data series, where only the diagonal slices of cumulants are used. The AR parameters are adjusted at each sample by using a successive over-relaxation (SOR) scheme, a variety of the ordinary LMS scheme, but with a faster convergence rate and a greater robustness to the selection of the ‘step-size’ in iterations. Computer simulations are implemented for both linear time-invariant (LTI) and linear time-variant (LTV) NMP channels, and the results show that the algorithm proposed in this paper has a fast convergence rate and a potential capability to track the LTV NMP channels.

Abundance and biomass of dinoflagellates and ciliates at time series station Helgoland Roads, North Sea, 2007-2009

A monitoring programme for microzooplankton was started at the long-term sampling station ''Kabeltonne'' at Helgoland Roads (54°11.30' N; 7°54.00' E) in January 2007 in order to provide more detailed knowledge on microzooplankton occurrence, composition and seasonality patterns at this site and to complement the existing plankton data series. Ciliate and dinoflagellate cell concentration and carbon biomass were recorded on a weekly basis. Heterotrophic dinoflagellates were considerably more important in terms of biomass than ciliates, especially during the summer months. However, in early spring, ciliates were the major group of microzooplankton grazers as they responded more quickly to phytoplankton food availability. Mixotrophic dinoflagellates played a secondary role in terms of biomass when compared to heterotrophic species; nevertheless, they made up an intense late summer bloom in 2007. The photosynthetic ciliate Myrionecta rubra bloomed at the end of the sampling period. Due to its high biomass when compared to crustacean plankton especially during the spring bloom, microzooplankton should be regarded as the more important phytoplankton grazer group at Helgoland Roads. Based on these results, analyses of biotic and abiotic factors driving microzooplankton composition and abundance are necessary for a full understanding of this important component of the plankton.

Palaeolake Rehwiese, NE-Germany, Younger Dryas varved data set RW09

The first 1400-year floating varve chronology for north-eastern Germany covering the late Allerød to the early Holocene has been established by microscopic varve counts from the Rehwiese palaeolake sediment record. The Laacher See Tephra (LST), at the base of the studied interval, forms the tephrochronological anchor point. The fine laminations were examined using a combination of micro-facies and ?-XRF analyses and are typical of calcite varves, which in this case provide mainly a warm season signal. Two varve types with different sub-layer structures have been distinguished: (I) complex varves consisting of up to four seasonal sub-layers formed during the Allerød and early Holocene periods, and, (II) simple two sub-layer type varves only occurring during the Younger Dryas. The precision of the chronology has been improved by varve-to-varve comparison of two independently analyzed sediment profiles based on well-defined micro-marker layers. This has enabled both (1) the precise location of single missing varves in one of the sediment profiles, and, (2) the verification of varve interpolation in disturbed varve intervals in the parallel core. Inter-annual and decadal-scale variability in sediment deposition processes were traced by multi-proxy data series including seasonal layer thickness, high-resolution element scans and total organic and inorganic carbon data at a five-varve resolution. These data support the idea of a two-phase Younger Dryas, with the first interval (12,675 - 12,275 varve years BP) characterised by a still significant but gradually decreasing warm-season calcite precipitation and a second phase (12,275 - 11,640 varve years BP) with only weak calcite precipitation. Detailed correlation of these two phases with the Meerfelder Maar record based on the LST isochrone and independent varve counts provides clues about regional differences and seasonal aspects of YD climate change along a transect from a location proximal to the North Atlantic in the west to a more continental site in the east.