218,583 research outputs found
An Interpretable Probabilistic Autoregressive Neural Network Model for Time Series Forecasting
Forecasting time series data presents an emerging field of data science that
has its application ranging from stock price and exchange rate prediction to
the early prediction of epidemics. Numerous statistical and machine learning
methods have been proposed in the last five decades with the demand for
generating high-quality and reliable forecasts. However, in real-life
prediction problems, situations exist in which a model based on one of the
above paradigms is preferable, and therefore, hybrid solutions are needed to
bridge the gap between classical forecasting methods and scalable neural
network models. We introduce an interpretable probabilistic autoregressive
neural network model for an explainable, scalable, and "white box-like"
framework that can handle a wide variety of irregular time series data (e.g.,
nonlinearity and nonstationarity). Sufficient conditions for asymptotic
stationarity and geometric ergodicity are obtained by considering the
asymptotic behavior of the associated Markov chain. During computational
experiments, PARNN outperforms standard statistical, machine learning, and deep
learning models on a diverse collection of real-world datasets coming from
economics, finance, and epidemiology, to mention a few. Furthermore, the
proposed PARNN model improves forecast accuracy significantly for 10 out of 12
datasets compared to state-of-the-art models for short to long-term forecasts
An Analytical Methodology To Security Constraints Management In Power System Operation
In a deregulated electricity market, Independent System Operators (ISOs) are responsible for dispatching power to the load securely, efficiently, and economically. ISO performs Security Constrained Unit Commitment (SCUC) to guarantee sufficient generation commitment, maximized social welfare and facilitating market-driven economics. A large number of security constraints would render the model impossible to solve under time requirements. Developing a method to identify the minimum set of security constraints without overcommitting is necessary to reduce Mixed Integer Linear Programming (MILP) solution time. To overcome this challenge, we developed a powerful tool called security constraint screening. The proposed approach effectively filters out non-dominating constraints by integrating virtual transactions and capturing changes online in real-time or look-ahead markets. The security-constraint screening takes advantage of both deterministic and statistical methods, which leverages mathematical modeling and historical data. Effectiveness is verified using Midcontinent Independent System Operator (MISO) data. The research also presented a data-driven approach to forecast congestion patterns in real-time utilizing machine learning applications. Studies have been conducted using real-world data. The potential benefit is to provide the day-ahead operators with a tool for supporting decision-making regarding modeling constraints
Performative Time-Series Forecasting
Time-series forecasting is a critical challenge in various domains and has
witnessed substantial progress in recent years. Many real-life scenarios, such
as public health, economics, and social applications, involve feedback loops
where predictions can influence the predicted outcome, subsequently altering
the target variable's distribution. This phenomenon, known as performativity,
introduces the potential for 'self-negating' or 'self-fulfilling' predictions.
Despite extensive studies in classification problems across domains,
performativity remains largely unexplored in the context of time-series
forecasting from a machine-learning perspective.
In this paper, we formalize performative time-series forecasting (PeTS),
addressing the challenge of accurate predictions when performativity-induced
distribution shifts are possible. We propose a novel approach, Feature
Performative-Shifting (FPS), which leverages the concept of delayed response to
anticipate distribution shifts and subsequently predicts targets accordingly.
We provide theoretical insights suggesting that FPS can potentially lead to
reduced generalization error. We conduct comprehensive experiments using
multiple time-series models on COVID-19 and traffic forecasting tasks. The
results demonstrate that FPS consistently outperforms conventional time-series
forecasting methods, highlighting its efficacy in handling
performativity-induced challenges.Comment: 12 pages (7 main text, 2 reference, 3 appendix), 3 figures, 4 table
Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market
We report successful results from using deep learning neural networks (DLNNs)
to learn, purely by observation, the behavior of profitable traders in an
electronic market closely modelled on the limit-order-book (LOB) market
mechanisms that are commonly found in the real-world global financial markets
for equities (stocks & shares), currencies, bonds, commodities, and
derivatives. Successful real human traders, and advanced automated algorithmic
trading systems, learn from experience and adapt over time as market conditions
change; our DLNN learns to copy this adaptive trading behavior. A novel aspect
of our work is that we do not involve the conventional approach of attempting
to predict time-series of prices of tradeable securities. Instead, we collect
large volumes of training data by observing only the quotes issued by a
successful sales-trader in the market, details of the orders that trader is
executing, and the data available on the LOB (as would usually be provided by a
centralized exchange) over the period that the trader is active. In this paper
we demonstrate that suitably configured DLNNs can learn to replicate the
trading behavior of a successful adaptive automated trader, an algorithmic
system previously demonstrated to outperform human traders. We also demonstrate
that DLNNs can learn to perform better (i.e., more profitably) than the trader
that provided the training data. We believe that this is the first ever
demonstration that DLNNs can successfully replicate a human-like, or
super-human, adaptive trader operating in a realistic emulation of a real-world
financial market. Our results can be considered as proof-of-concept that a DLNN
could, in principle, observe the actions of a human trader in a real financial
market and over time learn to trade equally as well as that human trader, and
possibly better.Comment: 8 pages, 4 figures. To be presented at IEEE Symposium on
Computational Intelligence in Financial Engineering (CIFEr), Bengaluru; Nov
18-21, 201
Learning Active Constraints to Efficiently Solve Linear Bilevel Problems
Bilevel programming can be used to formulate many engineering and economics
problems. However, common reformulations of bilevel problems to mixed-integer
linear programs (through the use of Karush-Kuhn-Tucker conditions) make solving
such problems hard, which impedes their implementation in real-life. In this
paper, we significantly improve solution speed and tractability by introducing
decision trees to learn the active constraints of the lower-level problem,
while avoiding to introduce binaries and big-M constants. The application of
machine learning reduces the online solving time, and becomes particularly
beneficial when the same problem has to be solved multiple times. We apply our
approach to power systems problems, and especially to the strategic bidding of
generators in electricity markets, where generators solve the same problem many
times for varying load demand or renewable production. Three methods are
developed and applied to the problem of a strategic generator, with a DCOPF in
the lower-level. We show that for networks of varying sizes, the computational
burden is significantly reduced, while we also manage to find solutions for
strategic bidding problems that were previously intractable.Comment: 11 pages, 5 figure
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