163 research outputs found
Multimodal analysis of verbal and nonverbal behaviour on the example of clinical depression
Clinical depression is a common mood disorder that may last for long periods, vary
in severity, and could impair an individual’s ability to cope with daily life. Depression
affects 350 million people worldwide and is therefore considered a burden not
only on a personal and social level, but also on an economic one. Depression is the
fourth most significant cause of suffering and disability worldwide and it is predicted
to be the leading cause in 2020.
Although treatment of depression disorders has proven to be effective in most
cases, misdiagnosing depressed patients is a common barrier. Not only because
depression manifests itself in different ways, but also because clinical interviews and
self-reported history are currently the only ways of diagnosis, which risks a range
of subjective biases either from the patient report or the clinical judgment. While
automatic affective state recognition has become an active research area in the past
decade, methods for mood disorder detection, such as depression, are still in their
infancy. Using the advancements of affective sensing techniques, the long-term goal
is to develop an objective multimodal system that supports clinicians during the
diagnosis and monitoring of clinical depression.
This dissertation aims to investigate the most promising characteristics of depression
that can be “heard” and “seen” by a computer system for the task of detecting
depression objectively. Using audio-video recordings of a clinically validated
Australian depression dataset, several experiments are conducted to characterise
depression-related patterns from verbal and nonverbal cues. Of particular interest in
this dissertation is the exploration of speech style, speech prosody, eye activity, and
head pose modalities. Statistical analysis and automatic classification of extracted
cues are investigated. In addition, multimodal fusion methods of these modalities
are examined to increase the accuracy and confidence level of detecting depression.
These investigations result in a proposed system that detects depression in a binary
manner (e.g. depressed vs. non-depressed) using temporal depression behavioural
cues.
The proposed system: (1) uses audio-video recordings to investigate verbal and
nonverbal modalities, (2) extracts functional features from verbal and nonverbal
modalities over the entire subjects’ segments, (3) pre- and post-normalises the extracted
features, (4) selects features using the T-test, (5) classifies depression in a
binary manner (i.e. severely depressed vs. healthy controls), and finally (6) fuses the
individual modalities.
The proposed system was validated for scalability and usability using generalisation
experiments. Close studies were made of American and German depression
datasets individually, and then also in combination with the Australian one. Applying
the proposed system to the three datasets showed remarkably high classification results - up to a 95% average recall for the individual sets and 86% for the three
combined. Strong implications are that the proposed system has the ability to generalise
to different datasets recorded under quite different conditions such as collection
procedure and task, depression diagnosis testing and scale, as well as cultural and
language background. High performance was found consistently in speech prosody
and eye activity in both individual and combined datasets, with head pose features
a little less remarkable. Strong indications are that the extracted features are robust
to large variations in recording conditions. Furthermore, once the modalities were
combined, the classification results improved substantially. Therefore, the modalities
are shown both to correlate and complement each other, working in tandem as an
innovative system for diagnoses of depression across large variations of population
and procedure
Adaptive Algorithms For Classification On High-Frequency Data Streams: Application To Finance
Mención Internacional en el título de doctorIn recent years, the problem of concept drift has gained importance in the financial
domain. The succession of manias, panics and crashes have stressed the nonstationary
nature and the likelihood of drastic structural changes in financial markets.
The most recent literature suggests the use of conventional machine learning and statistical
approaches for this. However, these techniques are unable or slow to adapt
to non-stationarities and may require re-training over time, which is computationally
expensive and brings financial risks.
This thesis proposes a set of adaptive algorithms to deal with high-frequency data
streams and applies these to the financial domain. We present approaches to handle
different types of concept drifts and perform predictions using up-to-date models.
These mechanisms are designed to provide fast reaction times and are thus applicable
to high-frequency data. The core experiments of this thesis are based on the prediction
of the price movement direction at different intraday resolutions in the SPDR S&P 500
exchange-traded fund. The proposed algorithms are benchmarked against other popular
methods from the data stream mining literature and achieve competitive results.
We believe that this thesis opens good research prospects for financial forecasting
during market instability and structural breaks. Results have shown that our proposed
methods can improve prediction accuracy in many of these scenarios. Indeed, the
results obtained are compatible with ideas against the efficient market hypothesis.
However, we cannot claim that we can beat consistently buy and hold; therefore, we
cannot reject it.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Gustavo Recio Isasi.- Secretario: Pedro Isasi Viñuela.- Vocal: Sandra García Rodrígue
Forecasting gold prices using temporal convolutional networks
Accurate prediction of the financial markets can provide
many benefits, of which underlying economic stability is probably the
most important. This area has understandably attracted a significant
amount of interest from the research community, and has inspired a diverse range of approaches with varying degrees of success. Gold is a particular commodity which has attracted considerable attention since it was first smelted for ornaments and jewellery by the Egyptians in 3600BC. In uncertain economic times it is regularly used as a safe-haven commodity, and is why there is considerable attention given to enhancing the accuracy of gold prices prediction methods. Previous attempts at gold price prediction have used a variety of econometric and machine learning techniques. In particular Long Short-Term Networks (LSTMs) and more recently an ensemble of Convolutional Neural Networks (CNNs) and LSTMs have been found to have had considerable level of success in time series prediction. In this research we have conducted a comparative analysis between ARIMA, CNN, LSTM and CNN-LSTM and a recently introduced structure known as Temporal Convolutional Networks (TCNs) on gold price data spanning 20 years.
The results show how TCNs produced a RMSE of 15.26 and outperformed both CNN-LSTM and LSTM with RMSE scores of 23.53 and 27.39 respectively
Investors Do Respond to Poor Mutual Fund Performance: Evidence from Inflows and Outflows
Abstract
We examine the relation between mutual fund performance and gross flows for a large sample of actively managed U.S. mutual funds. Unlike previous studies that have only examined periods of generally increasing net flows, our sample includes periods of both increasing and decreasing net flows. We find that outflows are related to performance, with investors withdrawing money from poor performers. We also find that outflows and inflows respond asymmetrically to performance, outflows increase more aggressively following poor performance, and inflows increase more aggressively following good performance. Additionally, we find a symmetric performance net flow relation
Stock Market Prediction via Deep Learning Techniques: A Survey
The stock market prediction has been a traditional yet complex problem
researched within diverse research areas and application domains due to its
non-linear, highly volatile and complex nature. Existing surveys on stock
market prediction often focus on traditional machine learning methods instead
of deep learning methods. Deep learning has dominated many domains, gained much
success and popularity in recent years in stock market prediction. This
motivates us to provide a structured and comprehensive overview of the research
on stock market prediction focusing on deep learning techniques. We present
four elaborated subtasks of stock market prediction and propose a novel
taxonomy to summarize the state-of-the-art models based on deep neural networks
from 2011 to 2022. In addition, we also provide detailed statistics on the
datasets and evaluation metrics commonly used in the stock market. Finally, we
highlight some open issues and point out several future directions by sharing
some new perspectives on stock market prediction
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