7 research outputs found
scatteR: Generating instance space based on scagnostics
Traditional synthetic data generation methods rely on model-based approaches
that tune the parameters of a model rather than focusing on the structure of
the data itself. In contrast, Scagnostics is an exploratory graphical method
that captures the structure of bivariate data using graph-theoretic measures.
This paper presents a novel data generation method, scatteR, that uses
Scagnostics measurements to control the characteristics of the generated
dataset. By using an iterative Generalized Simulated Annealing optimizer,
scatteR finds the optimal arrangement of data points that minimizes the
distance between current and target Scagnostics measurements. The results
demonstrate that scatteR can generate 50 data points in under 30 seconds with
an average Root Mean Squared Error of 0.05, making it a useful pedagogical tool
for teaching statistical methods. Overall, scatteR provides an entry point for
generating datasets based on the characteristics of instance space, rather than
relying on model-based simulations.Comment: 17 pages. For the associated R package, see
https://cran.r-project.org/package=scatte
FFORMPP: Feature-based forecast model performance prediction
This paper introduces a novel meta-learning algorithm for time series
forecast model performance prediction. We model the forecast error as a
function of time series features calculated from the historical time series
with an efficient Bayesian multivariate surface regression approach. The
minimum predicted forecast error is then used to identify an individual model
or a combination of models to produce the final forecasts. It is well-known
that the performance of most meta-learning models depends on the
representativeness of the reference dataset used for training. In such
circumstances, we augment the reference dataset with a feature-based time
series simulation approach, namely GRATIS, in generating a rich and
representative time series collection. The proposed framework is tested using
the M4 competition data and is compared against commonly used forecasting
approaches. Our approach provides comparable performances to other model
selection/combination approaches but at a lower computational cost and a higher
degree of interpretability, which is important for supporting decisions. We
also provide useful insights regarding which forecasting models are expected to
work better for particular types of time series, the intrinsic mechanisms of
the meta-learners and how the forecasting performances are affected by various
factors.Comment: 40 page
Forecasting: theory and practice
Forecasting has always been in the forefront of decision making and planning.
The uncertainty that surrounds the future is both exciting and challenging,
with individuals and organisations seeking to minimise risks and maximise
utilities. The lack of a free-lunch theorem implies the need for a diverse set
of forecasting methods to tackle an array of applications. This unique article
provides a non-systematic review of the theory and the practice of forecasting.
We offer a wide range of theoretical, state-of-the-art models, methods,
principles, and approaches to prepare, produce, organise, and evaluate
forecasts. We then demonstrate how such theoretical concepts are applied in a
variety of real-life contexts, including operations, economics, finance,
energy, environment, and social good. We do not claim that this review is an
exhaustive list of methods and applications. The list was compiled based on the
expertise and interests of the authors. However, we wish that our encyclopedic
presentation will offer a point of reference for the rich work that has been
undertaken over the last decades, with some key insights for the future of the
forecasting theory and practice
Forecasting: theory and practice
Forecasting has always been at the forefront of decision making and planning.
The uncertainty that surrounds the future is both exciting and challenging,
with individuals and organisations seeking to minimise risks and maximise
utilities. The large number of forecasting applications calls for a diverse set
of forecasting methods to tackle real-life challenges. This article provides a
non-systematic review of the theory and the practice of forecasting. We provide
an overview of a wide range of theoretical, state-of-the-art models, methods,
principles, and approaches to prepare, produce, organise, and evaluate
forecasts. We then demonstrate how such theoretical concepts are applied in a
variety of real-life contexts.
We do not claim that this review is an exhaustive list of methods and
applications. However, we wish that our encyclopedic presentation will offer a
point of reference for the rich work that has been undertaken over the last
decades, with some key insights for the future of forecasting theory and
practice. Given its encyclopedic nature, the intended mode of reading is
non-linear. We offer cross-references to allow the readers to navigate through
the various topics. We complement the theoretical concepts and applications
covered by large lists of free or open-source software implementations and
publicly-available databases
Forecasting: theory and practice
Forecasting has always been at the forefront of decision making and planning.
The uncertainty that surrounds the future is both exciting and challenging,
with individuals and organisations seeking to minimise risks and maximise
utilities. The large number of forecasting applications calls for a diverse set
of forecasting methods to tackle real-life challenges. This article provides a
non-systematic review of the theory and the practice of forecasting. We provide
an overview of a wide range of theoretical, state-of-the-art models, methods,
principles, and approaches to prepare, produce, organise, and evaluate
forecasts. We then demonstrate how such theoretical concepts are applied in a
variety of real-life contexts.
We do not claim that this review is an exhaustive list of methods and
applications. However, we wish that our encyclopedic presentation will offer a
point of reference for the rich work that has been undertaken over the last
decades, with some key insights for the future of forecasting theory and
practice. Given its encyclopedic nature, the intended mode of reading is
non-linear. We offer cross-references to allow the readers to navigate through
the various topics. We complement the theoretical concepts and applications
covered by large lists of free or open-source software implementations and
publicly-available databases