11 research outputs found
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
A survey on machine learning for recurring concept drifting data streams
The problem of concept drift has gained a lot of attention in recent years. This aspect is key in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks affecting their generative processes. In this survey, we review the relevant literature to deal with regime changes in the behaviour of continuous data streams. The study starts with a general introduction to the field of data stream learning, describing recent works on passive or active mechanisms to adapt or detect concept drifts, frequent challenges in this area, and related performance metrics. Then, different supervised and non-supervised approaches such as online ensembles, meta-learning and model-based clustering that can be used to deal with seasonalities in a data stream are covered. The aim is to point out new research trends and give future research directions on the usage of machine learning techniques for data streams which can help in the event of shifts and recurrences in continuous learning scenarios in near real-time
An incremental clustering and associative learning architecture for intelligent robotics
The ability to learn from the environment and memorise the acquired knowledge is
essential for robots to become autonomous and versatile artificial companions. This
thesis proposes a novel learning and memory architecture for robots, which performs
associative learning and recall of sensory and actuator patterns. The approach
avoids the inclusion of task-specific expert knowledge and can deal with any kind of
multi-dimensional real-valued data, apart from being tolerant to noise and supporting
incremental learning. The proposed architecture integrates two machine learning
methods: a topology learning algorithm that performs incremental clustering, and
an associative memory model that learns relationship information based on the
co-occurrence of inputs.
The evaluations of both the topology learning algorithm and the associative
memory model involved the memorisation of high-dimensional visual data as well as
the association of symbolic data, presented simultaneously and sequentially. Moreover,
the document analyses the results of two experiments in which the entire architecture
was evaluated regarding its associative and incremental learning capabilities. One
experiment comprised an incremental learning task with visual patterns and text
labels, which was performed both in a simulated scenario and with a real robot. In a
second experiment a robot learned to recognise visual patterns in the form of road
signs and associated them with di erent con gurations of its arm joints.
The thesis also discusses several learning-related aspects of the architecture
and highlights strengths and weaknesses of the proposed approach. The developed
architecture and corresponding ndings contribute to the domains of machine learning
and intelligent robotics
Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic Review
Recent crises, recessions and bubbles have stressed the non-stationary nature and the presence of drastic structural changes in the financial domain. The most recent literature suggests the use of conventional machine learning and statistical approaches in this context. Unfortunately, several of these techniques are unable or slow to adapt to changes in the price-generation process. This study aims to survey the relevant literature on Machine Learning for financial prediction under regime change employing a systematic approach.
It reviews key papers with a special emphasis on technical analysis. The study discusses the growing number of contributions that are bridging the gap between two separate communities, one focused on data stream learning and the other on economic research. However, it also makes apparent that we are still in an early stage. The range of machine learning algorithms that have been tested in this domain is very wide, but the results of the study do not suggest that currently there is a specific technique that is clearly dominant
Class-incremental lifelong object learning for domestic robots
Traditionally, robots have been confined to settings where they operate in isolation and in highly
controlled and structured environments to execute well-defined non-varying tasks. As a result,
they usually operate without the need to perceive their surroundings or to adapt to changing
stimuli. However, as robots start to move towards human-centred environments and share the
physical space with people, there is an urgent need to endow them with the flexibility to learn
and adapt given the changing nature of the stimuli they receive and the evolving requirements
of their users. Standard machine learning is not suitable for these types of applications because
it operates under the assumption that data samples are independent and identically distributed,
and requires access to all the data in advance. If any of these assumptions is broken, the model
fails catastrophically, i.e., either it does not learn or it forgets all that was previously learned.
Therefore, different strategies are required to address this problem.
The focus of this thesis is on lifelong object learning, whereby a model is able to learn
from data that becomes available over time. In particular we address the problem of classincremental learning with an emphasis on algorithms that can enable interactive learning with
a user. In class-incremental learning, models learn from sequential data batches where each
batch can contain samples coming from ideally a single class. The emphasis on interactive
learning capabilities poses additional requirements in terms of the speed with which model
updates are performed as well as how the interaction is handled.
The work presented in this thesis can be divided into two main lines of work. First,
we propose two versions of a lifelong learning algorithm composed of a feature extractor
based on pre-trained residual networks, an array of growing self-organising networks and a
classifier. Self-organising networks are able to adapt their structure based on the input data
distribution, and learn representative prototypes of the data. These prototypes can then be
used to train a classifier. The proposed approaches are evaluated on various benchmarks under
several conditions and the results show that they outperform competing approaches in each
case. Second, we propose a robot architecture to address lifelong object learning through
interactions with a human partner using natural language. The architecture consists of an
object segmentation, tracking and preprocessing pipeline, a dialogue system, and a learning
module based on the algorithm developed in the first part of the thesis. Finally, the thesis also
includes an exploration into the contributions that different preprocessing operations have on
performance when learning from both RGB and Depth images.James Watt Scholarshi
Contributions of synaptic filters to models of synaptically stored memory
The question of how neural systems encode memories in one-shot without immediately disrupting previously stored information has puzzled theoretical neuroscientists for years and it is the central topic of this thesis. Previous attempts on this topic, have proposed that synapses probabilistically update in response to plasticity inducing stimuli to effectively delay the degradation of old memories in the face of ongoing memory storage. Indeed, experiments have shown that synapses do not immediately respond to plasticity inducing stimuli, since these must be presented many times before synaptic plasticity is expressed. Such a delay could be due to the stochastic nature of synaptic plasticity or perhaps because induction signals are integrated before overt strength changes occur.The later approach has been previously applied to control fluctuations in neural development by low-pass filtering induction signals before plasticity is expressed. In this thesis we consider memory dynamics in a mathematical model with synapses that integrate plasticity induction signals to a threshold before expressing plasticity. We report novel recall dynamics and considerable improvements in memory lifetimes against a prominent model of synaptically stored memory. With integrating synapses the memory trace initially rises before reaching a maximum and then falls. The memory signal dissociates into separate oblivescence and reminiscence components, with reminiscence initially dominating recall. Furthermore, we find that integrating synapses possess natural timescales that can be used to consider the transition to late-phase plasticity under spaced repetition patterns known to lead to optimal storage conditions. We find that threshold crossing statistics differentiate between massed and spaced memory repetition patterns. However, isolated integrative synapses obtain an insufficient statistical sample to detect the stimulation pattern within a few memory repetitions. We extend the modelto consider the cooperation of well-known intracellular signalling pathways in detecting storage conditions by utilizing the profile of postsynaptic depolarization. We find that neuron wide signalling and local synaptic signals can be combined to detect optimal storage conditions that lead to stable forms of plasticity in a synapse specific manner.These models can be further extended to consider heterosynaptic and neuromodulatory interactions for late-phase plasticity.<br/
Dynamic Generalisation of Continuous Action Spaces in Reinforcement Learning: A Neurally Inspired Approach
Institute for Adaptive and Neural ComputationAward number: 98318242.This thesis is about the dynamic generalisation of continuous action spaces in
reinforcement learning problems.
The standard Reinforcement Learning (RL) account provides a principled and comprehensive
means of optimising a scalar reward signal in a Markov Decision Process.
However, the theory itself does not directly address the imperative issue of generalisation
which naturally arises as a consequence of large or continuous state and action
spaces. A current thrust of research is aimed at fusing the generalisation capabilities
of supervised (and unsupervised) learning techniques with the RL theory. An example
par excellence is Tesauro’s TD-Gammon.
Although much effort has gone into researching ways to represent and generalise over
the input space, much less attention has been paid to the action space. This thesis
first considers the motivation for learning real-valued actions, and then proposes a
set of key properties desirable in any candidate algorithm addressing generalisation
of both input and action spaces. These properties include: Provision of adaptive and
online generalisation, adherence to the standard theory with a central focus on estimating
expected reward, provision for real-valued states and actions, and full support
for a real-valued discounted reward signal. Of particular interest are issues pertaining
to robustness in non-stationary environments, scalability, and efficiency for real-time
learning in applications such as robotics. Since exploring the action space is discovered
to be a potentially costly process, the system should also be flexible enough to
enable maximum reuse of learned actions.
A new approach is proposed which succeeds for the first time in addressing all of the
key issues identified. The algorithm, which is based on the ubiquitous self-organising
map, is analysed and compared with other techniques including those based on the
backpropagation algorithm. The investigation uncovers some important implications
of the differences between these two particular approaches with respect to RL. In particular,
the distributed representation of the multi-layer perceptron is judged to be
something of a double-edged sword offering more sophisticated and more scalable
generalising power, but potentially causing problems in dynamic or non-equiprobable
environments, and tasks involving a highly varying input-output mapping.
The thesis concludes that the self-organising map can be used in conjunction with current
RL theory to provide real-time dynamic representation and generalisation of continuous
action spaces. The proposed model is shown to be reliable in non-stationary,
unpredictable and noisy environments and judged to be unique in addressing and satisfying
a number of desirable properties identified as important to a large class of RL
problems