Bitcoin has recently attracted considerable attention in the fields of economics, cryptography, and computer science due to its inherent nature of combining encryption technology and monetary units. ...This paper reveals the effect of Bayesian neural networks (BNNs) by analyzing the time series of Bitcoin process. We also select the most relevant features from Blockchain information that is deeply involved in Bitcoin's supply and demand and use them to train models to improve the predictive performance of the latest Bitcoin pricing process. We conduct the empirical study that compares the Bayesian neural network with other linear and non-linear benchmark models on modeling and predicting the Bitcoin process. Our empirical studies show that BNN performs well in predicting Bitcoin price time series and explaining the high volatility of the recent Bitcoin price.
SUMMARY
Late-time transient electromagnetic (TEM) data contain deep subsurface information and are important for resolving deeper electrical structures. However, due to their relatively small signal ...amplitudes, TEM responses later in time are often dominated by ambient noises. Therefore, noise removal is critical to the application of TEM data in imaging electrical structures at depth. De-noising techniques for TEM data have been developed rapidly in recent years. Although strong efforts have been made to improving the quality of the TEM responses, it is still a challenge to effectively extract the signals due to unpredictable and irregular noises. In this study, we develop a new type of neural network architecture by combining the long short-term memory (LSTM) network with the autoencoder structure to suppress noise in TEM signals. The resulting LSTM-autoencoders yield excellent performance on synthetic data sets including horizontal components of the electric field and vertical component of the magnetic field generated by different sources such as dipole, loop and grounded line sources. The relative errors between the de-noised data sets and the corresponding noise-free transients are below 1% for most of the sampling points. Notable improvement in the resistivity structure inversion result is achieved using the TEM data de-noised by the LSTM-autoencoder in comparison with several widely-used neural networks, especially for later-arriving signals that are important for constraining deeper structures. We demonstrate the effectiveness and general applicability of the LSTM-autoencoder by de-noising experiments using synthetic 1-D and 3-D TEM signals as well as field data sets. The field data from a fixed loop survey using multiple receivers are greatly improved after de-noising by the LSTM-autoencoder, resulting in more consistent inversion models with significantly increased exploration depth. The LSTM-autoencoder is capable of enhancing the quality of the TEM signals at later times, which enables us to better resolve deeper electrical structures.
•Examined associations of emotion dynamics and mood psychopathology longitudinally.•Emotion instability associated with bipolar spectrum at baseline and follow-up.•Emotion instability predicted ...development of new bipolar spectrum diagnoses.•Depression associated with negative emotion instability at baseline.
Altered emotion dynamics may represent a transdiagnostic risk factor for mood psychopathology. The present study examined whether altered emotion dynamics were associated with bipolar and depressive psychopathology concurrently and at a three-year follow-up.
At baseline (n = 138), participants completed diagnostic interviews, questionnaires, and seven days of experience sampling assessments. Four emotion dynamics were computed for negative affect (NA) and positive affect (PA) – within-person variance (variability), mean square of successive differences and probability of acute change (instability), and autocorrelation (inertia). At the three-year follow-up, participants (n = 108) were re-assessed via interviews and questionnaires.
NA variability was associated with bipolar spectrum disorders at baseline and follow-up. NA instability predicted depressive symptoms and hypomanic personality at baseline, and bipolar spectrum disorders at the follow-up. NA inertia did not predict diagnoses or symptoms at either assessment. PA inertia predicted hyperthymic temperament at baseline but not follow-up. Notably, NA variability and instability predicted the development of new bipolar spectrum disorders at the follow-up.
Consistent with the recruitment strategy and young age of the participants, only 50% had developed diagnosable psychopathology by the time of the follow-up assessment.
The present study provided a unique demonstration that altered emotion dynamics differentially predicted bipolar and depressive psychopathology concurrently and prospectively. Emotion dynamics are important to both digital phenotyping and mobile-based interventions as emotional instability offers a measurable risk factor that is identifiable prior to illness onset.
SUMMARY
Effective and efficient arrival picking plays an important role in microseismic and earthquake data processing and imaging. Widely used short-term-average long-term-average ratio (STA/LTA) ...based arrival picking algorithms suffer from the sensitivity to moderate-to-strong random ambient noise. To make the state-of-the-art arrival picking approaches effective, microseismic data need to be first pre-processed, for example, removing sufficient amount of noise, and second analysed by arrival pickers. To conquer the noise issue in arrival picking for weak microseismic or earthquake event, I leverage the machine learning techniques to help recognizing seismic waveforms in microseismic or earthquake data. Because of the dependency of supervised machine learning algorithm on large volume of well-designed training data, I utilize an unsupervised machine learning algorithm to help cluster the time samples into two groups, that is, waveform points and non-waveform points. The fuzzy clustering algorithm has been demonstrated to be effective for such purpose. A group of synthetic, real microseismic and earthquake data sets with different levels of complexity show that the proposed method is much more robust than the state-of-the-art STA/LTA method in picking microseismic events, even in the case of moderately strong background noise.
The UCR time series archive Dau, Hoang Anh; Bagnall, Anthony; Kamgar, Kaveh ...
IEEE/CAA journal of automatica sinica,
11/2019, Volume:
6, Issue:
6
Journal Article
Peer reviewed
The UCR time series archive–introduced in 2002, has become an important resource in the time series data mining community, with at least one thousand published papers making use of at least one data ...set from the archive. The original incarnation of the archive had sixteen data sets but since that time, it has gone through periodic expansions. The last expansion took place in the summer of 2015 when the archive grew from 45 to 85 data sets. This paper introduces and will focus on the new data expansion from 85 to 128 data sets. Beyond expanding this valuable resource, this paper offers pragmatic advice to anyone who may wish to evaluate a new algorithm on the archive. Finally, this paper makes a novel and yet actionable claim: of the hundreds of papers that show an improvement over the standard baseline ( 1-nearest neighbor classification ), a fraction might be mis-attributing the reasons for their improvement. Moreover, the improvements claimed by these papers might have been achievable with a much simpler modification, requiring just a few lines of code.
SUMMARY
The detection and characterization of signals of interest in the presence of (in)coherent ambient noise is central to the analysis of infrasound array data. Microbaroms have an extended ...source region and a dynamical character. From the perspective of an infrasound array, these coherent noise sources appear as interfering signals that conventional beamform methods may not correctly resolve. This limits the ability of an infrasound array to dissect the incoming wavefield into individual components. In this paper, this problem will be addressed by proposing a high-resolution beamform technique in combination with the CLEAN algorithm. CLEAN iteratively selects the maximum of the f/k spectrum (i.e. following the Bartlett or the Capon method) and removes a percentage of the corresponding signal from the cross-spectral density matrix. In this procedure, the array response is deconvolved from the f/k spectral density function. The spectral peaks are retained in a ‘clean’ spectrum. A data-driven stopping criterion for CLEAN is proposed, which relies on the framework of Fisher statistics. This allows the construction of an automated algorithm that continuously extracts coherent energy until the point is reached that only incoherent noise is left in the data. CLEAN is tested on a synthetic data set and is applied to data from multiple International Monitoring System infrasound arrays. The results show that the proposed method allows for the identification of multiple microbarom source regions in the Northern Atlantic that would have remained unidentified if conventional methods had been applied.
Contemporary climate change in Alaska has resulted in amplified rates of press and pulse disturbances that drive ecosystem change with significant consequences for socio‐environmental systems. ...Despite the vulnerability of Arctic and boreal landscapes to change, little has been done to characterize landscape change and associated drivers across northern high‐latitude ecosystems. Here we characterize the historical sensitivity of Alaska's ecosystems to environmental change and anthropogenic disturbances using expert knowledge, remote sensing data, and spatiotemporal analyses and modeling. Time‐series analysis of moderate—and high‐resolution imagery was used to characterize land‐ and water‐surface dynamics across Alaska. Some 430,000 interpretations of ecological and geomorphological change were made using historical air photos and satellite imagery, and corroborate land‐surface greening, browning, and wetness/moisture trend parameters derived from peak‐growing season Landsat imagery acquired from 1984 to 2015. The time series of change metrics, together with climatic data and maps of landscape characteristics, were incorporated into a modeling framework for mapping and understanding of drivers of change throughout Alaska. According to our analysis, approximately 13% (~174,000 ± 8700 km2) of Alaska has experienced directional change in the last 32 years (±95% confidence intervals). At the ecoregions level, substantial increases in remotely sensed vegetation productivity were most pronounced in western and northern foothills of Alaska, which is explained by vegetation growth associated with increasing air temperatures. Significant browning trends were largely the result of recent wildfires in interior Alaska, but browning trends are also driven by increases in evaporative demand and surface‐water gains that have predominately occurred over warming permafrost landscapes. Increased rates of photosynthetic activity are associated with stabilization and recovery processes following wildfire, timber harvesting, insect damage, thermokarst, glacial retreat, and lake infilling and drainage events. Our results fill a critical gap in the understanding of historical and potential future trajectories of change in northern high‐latitude regions.
Arctic and boreal landscapes have experienced unprecedented changes in recent decades that have significant consequences for socio‐environmental systems. Despite a legacy of studies that have documented the heightened sensitivity of northern landscapes to change, characterization and prognosis of ecosystem change has remained elusive. Here, we combine remote sensing and climate reanalysis data into an integrated modeling framework to fingerprint the historical (1984–2015) sensitivity of Alaska's ecosystems to changing environmental conditions and disturbances. Our results fill a critical gap in the understanding of the historical and potential future trajectories of change in Alaska, with direct relevance to other northern high‐latitude regions.
The aim of this work is to build a methodology for representing urban regions using service-specific mobile traffic data from the Netmob dataset. Despite a rich literature on the topic, this kind of ...data has never been used to this purpose and it can therefore provide valuable insights into the dynamics and patterns of urban activity that were previously unexplored. Key steps include embedding time series data through autoencoding and utilizing different aggregation techniques to build geographically contextualized representations. As a further step we explore possible solutions to synthetically generate this kind of data in new urban areas.
SUMMARY
In a recent study, we showed that convolutional neural networks (CNNs) applied to network seismic traces can be used for rapid prediction of earthquake peak ground motion intensity measures ...(IMs) at distant stations using only recordings from stations near the epicentre. The predictions are made without any previous knowledge concerning the earthquake location and magnitude. This approach differs significantly from the standard procedure adopted by earthquake early warning systems that rely on location and magnitude information. In the previous study, we used 10 s, raw, multistation (39 stations) waveforms for the 2016 earthquake sequence in central Italy for 915 M ≥ 3.0 events (CI data set). The CI data set has a large number of spatially concentrated earthquakes and a dense network of stations. In this work, we applied the same CNN model to an area of central western Italy. In our initial application of the technique, we used a data set consisting of 266 M ≥ 3.0 earthquakes recorded by 39 stations. We found that the CNN model trained using this smaller-sized data set performed worse compared to the results presented in the previously published study. To counter the lack of data, we explored the adoption of ‘transfer learning’ (TL) methodologies using two approaches: first, by using a pre-trained model built on the CI data set and, next, by using a pre-trained model built on a different (seismological) problem that has a larger data set available for training. We show that the use of TL improves the results in terms of outliers, bias and variability of the residuals between predicted and true IM values. We also demonstrate that adding knowledge of station relative positions as an additional layer in the neural network improves the results. The improvements achieved through the experiments were demonstrated by the reduction of the number of outliers by 5 per cent, the residuals R median by 39 per cent and their standard deviation by 11 per cent.
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