Empirical analysis of daily cash flow time series and its implications for forecasting

Usual assumptions on the statistical properties of daily net cash flows include normality,absence of correlation and stationarity. We provide a comprehensive study based on a real-world cash flow data set showing that: (i) the usual assumption of normality, absence of correlation and stationarity hardly appear; (ii) non-linearity is often relevant for forecasting; and (iii) typical data transformations have little impact on linearity and normality. This evidence may lead to consider a more data-driven approach such as time-series forecasting in an attempt to provide cash managers with expert systems in cash management

Sono-electrodeposition transfer of micro-scale copper patterns on to A7 substrates using a mask-less method

A patterned anode tool was used to transfer electrodeposited microstructures on to an un-patterned A7 sized cathode by maintaining very narrow separation (300 µm) between the two electrodes and agitating the fluid in the inter-electrode gap by ultrasonic means. A non-acidic copper solution with a low content of metal ions and additives was used. Limiting current experiments were initially performed to demonstrate that improved and uniform agitation could be maintained within the inter-electrode gap at relatively low ultrasonic powers of 5 to 30 W L-1. The best pattern definition was obtained at a US power of 5 W L-1 and a current density of 20 mA cm-2. Importantly, the results obtained were comparable to those obtained by conventional through-mask plating. A single anode tool could be used to pattern up to five substrates, substantially minimising the amount of lithographic processing required. These results suggest that the proposed technique is a useful mask-less microfabrication process for pattern transfer on to large substrates

DSCo-NG: A Practical Language Modeling Approach for Time Series Classification

The abundance of time series data in various domains and their high dimensionality characteristic are challenging for harvesting useful information from them. To tackle storage and processing challenges, compression-based techniques have been proposed. Our previous work, Domain Series Corpus (DSCo), compresses time series into symbolic strings and takes advantage of language modeling techniques to extract from the training set knowledge about different classes. However, this approach was flawed in practice due to its excessive memory usage and the need for a priori knowledge about the dataset. In this paper we propose DSCo-NG, which reduces DSCo’s complexity and offers an efficient (linear time complexity and low memory footprint), accurate (performance comparable to approaches working on uncompressed data) and generic (so that it can be applied to various domains) approach for time series classification. Our confidence is backed with extensive experimental evaluation against publicly accessible datasets, which also offers insights on when DSCo-NG can be a better choice than others

Green electrochemical template synthesis of CoPt nanoparticles with tunable size, composition, and magnetism from microemulsions using an ionic liquid (bmimPF6)

Altres ajuts: Substrates have been prepared in IMB-CNM (CSIC),supported by the (CSIC) NGG-258 project.Electrodeposition from microemulsions using ionic liquids is revealed as a green method for synthesizing magnetic alloyed nanoparticles, avoiding the use of aggressive reducing agents. Microemulsions containing droplets of aqueous solution (electrolytic solution containing Pt(IV) and Co(II) ions) in an ionic liquid (bmimPF) define nanoreactors in which the electrochemical reduction takes place. Highly crystalline hcp alloyed CoPt nanoparticles, in the 10-120 nm range with a rather narrow size distribution, have been deposited on a conductive substrate. The relative amount of aqueous solution to ionic liquid determines the size of the nanoreactors, which serve as nanotemplates for the growth of the nanoparticles and hence determine their size and distribution. Further, the stoichiometry (PtCo) of the particles can be tuned by the composition of the electrolytic solution inside the droplets. The control of the size and composition of the particles allows tailoring the room-temperature magnetic behavior of the nanoparticles from superparaparamagnetic to hard magnetic (with a coercivity of H = 4100 Oe) in the as-obtained state. © 2014 American Chemical Society

Self-labeling techniques for semi-supervised time series classification: an empirical study

An increasing amount of unlabeled time series data available render the semi-supervised paradigm a suitable approach to tackle classification problems with a reduced quantity of labeled data. Self-labeled techniques stand out from semi-supervised classification methods due to their simplicity and the lack of strong assumptions about the distribution of the labeled and unlabeled data. This paper addresses the relevance of these techniques in the time series classification context by means of an empirical study that compares successful self-labeled methods in conjunction with various learning schemes and dissimilarity measures. Our experiments involve 35 time series datasets with different ratios of labeled data, aiming to measure the transductive and inductive classification capabilities of the self-labeled methods studied. The results show that the nearest-neighbor rule is a robust choice for the base classifier. In addition, the amending and multi-classifier self-labeled-based approaches reveal a promising attempt to perform semi-supervised classification in the time series context

A computational study on outliers in world music

The comparative analysis of world music cultures has been the focus of several ethnomusicological studies in the last century. With the advances of Music Information Retrieval and the increased accessibility of sound archives, large-scale analysis of world music with computational tools is today feasible. We investigate music similarity in a corpus of 8200 recordings of folk and traditional music from 137 countries around the world. In particular, we aim to identify music recordings that are most distinct compared to the rest of our corpus. We refer to these recordings as ‘outliers’. We use signal processing tools to extract music information from audio recordings, data mining to quantify similarity and detect outliers, and spatial statistics to account for geographical correlation. Our findings suggest that Botswana is the country with the most distinct recordings in the corpus and China is the country with the most distinct recordings when considering spatial correlation. Our analysis includes a comparison of musical attributes and styles that contribute to the ‘uniqueness’ of the music of each country

Zipf's Law in Short-Time Timbral Codings of Speech, Music, and Environmental Sound Signals

Timbre is a key perceptual feature that allows discrimination between different sounds. Timbral sensations are highly dependent on the temporal evolution of the power spectrum of an audio signal. In order to quantitatively characterize such sensations, the shape of the power spectrum has to be encoded in a way that preserves certain physical and perceptual properties. Therefore, it is common practice to encode short-time power spectra using psychoacoustical frequency scales. In this paper, we study and characterize the statistical properties of such encodings, here called timbral code-words. In particular, we report on rank-frequency distributions of timbral code-words extracted from 740 hours of audio coming from disparate sources such as speech, music, and environmental sounds. Analogously to text corpora, we find a heavy-tailed Zipfian distribution with exponent close to one. Importantly, this distribution is found independently of different encoding decisions and regardless of the audio source. Further analysis on the intrinsic characteristics of most and least frequent code-words reveals that the most frequent code-words tend to have a more homogeneous structure. We also find that speech and music databases have specific, distinctive code-words while, in the case of the environmental sounds, this database-specific code-words are not present. Finally, we find that a Yule-Simon process with memory provides a reasonable quantitative approximation for our data, suggesting the existence of a common simple generative mechanism for all considered sound sources