6,459 research outputs found
Housing Market Crash Prediction Using Machine Learning and Historical Data
The 2008 housing crisis was caused by faulty banking policies and the use of credit derivatives of mortgages for investment purposes. In this project, we look into datasets that are the markers to a typical housing crisis. Using those data sets we build three machine learning techniques which are, Linear regression, Hidden Markov Model, and Long Short-Term Memory. After building the model we did a comparative study to show the prediction done by each model. The linear regression model did not predict a housing crisis, instead, it showed that house prices would be rising steadily and the R-squared score of the model is 0.76. The Hidden Markov Model predicted a fall in the house prices and the R-squared score for this model is 0.706. Lastly, the Long Short-Term Memory showed that the house price would fall briefly but would stabilize after that. Also, fall is not as sharp as what was predicted by the HMM model. The R- squared scored for this model is 0.9, which is the highest among all other models. Although the R-squared score doesn’t say how accurate a model it definitely says how closely a model fits the data. From our model R-square score the model that best fits the data was LSTM. As the dataset used in all the models are the same therefore it is safe to say the prediction made by LSTM is better than the other ones
Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments
Eliminating the negative effect of non-stationary environmental noise is a
long-standing research topic for automatic speech recognition that stills
remains an important challenge. Data-driven supervised approaches, including
ones based on deep neural networks, have recently emerged as potential
alternatives to traditional unsupervised approaches and with sufficient
training, can alleviate the shortcomings of the unsupervised methods in various
real-life acoustic environments. In this light, we review recently developed,
representative deep learning approaches for tackling non-stationary additive
and convolutional degradation of speech with the aim of providing guidelines
for those involved in the development of environmentally robust speech
recognition systems. We separately discuss single- and multi-channel techniques
developed for the front-end and back-end of speech recognition systems, as well
as joint front-end and back-end training frameworks
SECaps: A Sequence Enhanced Capsule Model for Charge Prediction
Automatic charge prediction aims to predict appropriate final charges
according to the fact descriptions for a given criminal case. Automatic charge
prediction plays a critical role in assisting judges and lawyers to improve the
efficiency of legal decisions, and thus has received much attention.
Nevertheless, most existing works on automatic charge prediction perform
adequately on high-frequency charges but are not yet capable of predicting
few-shot charges with limited cases. In this paper, we propose a Sequence
Enhanced Capsule model, dubbed as SECaps model, to relieve this problem.
Specifically, following the work of capsule networks, we propose the seq-caps
layer, which considers sequence information and spatial information of legal
texts simultaneously. Then we design a attention residual unit, which provides
auxiliary information for charge prediction. In addition, our SECaps model
introduces focal loss, which relieves the problem of imbalanced charges.
Comparing the state-of-the-art methods, our SECaps model obtains 4.5% and 6.4%
absolutely considerable improvements under Macro F1 in Criminal-S and
Criminal-L respectively. The experimental results consistently demonstrate the
superiorities and competitiveness of our proposed model.Comment: 13 pages, 3figures, 5 table
Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks
We consider the problem of estimating the remaining useful life (RUL) of a
system or a machine from sensor data. Many approaches for RUL estimation based
on sensor data make assumptions about how machines degrade. Additionally,
sensor data from machines is noisy and often suffers from missing values in
many practical settings. We propose Embed-RUL: a novel approach for RUL
estimation from sensor data that does not rely on any degradation-trend
assumptions, is robust to noise, and handles missing values. Embed-RUL utilizes
a sequence-to-sequence model based on Recurrent Neural Networks (RNNs) to
generate embeddings for multivariate time series subsequences. The embeddings
for normal and degraded machines tend to be different, and are therefore found
to be useful for RUL estimation. We show that the embeddings capture the
overall pattern in the time series while filtering out the noise, so that the
embeddings of two machines with similar operational behavior are close to each
other, even when their sensor readings have significant and varying levels of
noise content. We perform experiments on publicly available turbofan engine
dataset and a proprietary real-world dataset, and demonstrate that Embed-RUL
outperforms the previously reported state-of-the-art on several metrics.Comment: Presented at 2nd ML for PHM Workshop at SIGKDD 2017, Halifax, Canad
Exploring Interpretable LSTM Neural Networks over Multi-Variable Data
For recurrent neural networks trained on time series with target and
exogenous variables, in addition to accurate prediction, it is also desired to
provide interpretable insights into the data. In this paper, we explore the
structure of LSTM recurrent neural networks to learn variable-wise hidden
states, with the aim to capture different dynamics in multi-variable time
series and distinguish the contribution of variables to the prediction. With
these variable-wise hidden states, a mixture attention mechanism is proposed to
model the generative process of the target. Then we develop associated training
methods to jointly learn network parameters, variable and temporal importance
w.r.t the prediction of the target variable. Extensive experiments on real
datasets demonstrate enhanced prediction performance by capturing the dynamics
of different variables. Meanwhile, we evaluate the interpretation results both
qualitatively and quantitatively. It exhibits the prospect as an end-to-end
framework for both forecasting and knowledge extraction over multi-variable
data.Comment: Accepted to International Conference on Machine Learning (ICML), 201
A Novel Distributed Representation of News (DRNews) for Stock Market Predictions
In this study, a novel Distributed Representation of News (DRNews) model is
developed and applied in deep learning-based stock market predictions. With the
merit of integrating contextual information and cross-documental knowledge, the
DRNews model creates news vectors that describe both the semantic information
and potential linkages among news events through an attributed news network.
Two stock market prediction tasks, namely the short-term stock movement
prediction and stock crises early warning, are implemented in the framework of
the attention-based Long Short Term-Memory (LSTM) network. It is suggested that
DRNews substantially enhances the results of both tasks comparing with five
baselines of news embedding models. Further, the attention mechanism suggests
that short-term stock trend and stock market crises both receive influences
from daily news with the former demonstrates more critical responses on the
information related to the stock market {\em per se}, whilst the latter draws
more concerns on the banking sector and economic policies.Comment: 25 page
Wind Power Forecasting Methods Based on Deep Learning: A Survey
Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics
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