9 research outputs found
ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-Step-Ahead Time-Series Forecasting
Recurrent and convolutional neural networks are the most common architectures
used for time series forecasting in deep learning literature. These networks
use parameter sharing by repeating a set of fixed architectures with fixed
parameters over time or space. The result is that the overall architecture is
time-invariant (shift-invariant in the spatial domain) or stationary. We argue
that time-invariance can reduce the capacity to perform multi-step-ahead
forecasting, where modelling the dynamics at a range of scales and resolutions
is required. We propose ForecastNet which uses a deep feed-forward architecture
to provide a time-variant model. An additional novelty of ForecastNet is
interleaved outputs, which we show assist in mitigating vanishing gradients.
ForecastNet is demonstrated to outperform statistical and deep learning
benchmark models on several datasets
Neural forecasting: Introduction and literature overview
Neural network based forecasting methods have become ubiquitous in
large-scale industrial forecasting applications over the last years. As the
prevalence of neural network based solutions among the best entries in the
recent M4 competition shows, the recent popularity of neural forecasting
methods is not limited to industry and has also reached academia. This article
aims at providing an introduction and an overview of some of the advances that
have permitted the resurgence of neural networks in machine learning. Building
on these foundations, the article then gives an overview of the recent
literature on neural networks for forecasting and applications.Comment: 66 pages, 5 figure
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