1,217 research outputs found
Forecasting with time series imaging
Feature-based time series representations have attracted substantial
attention in a wide range of time series analysis methods. Recently, the use of
time series features for forecast model averaging has been an emerging research
focus in the forecasting community. Nonetheless, most of the existing
approaches depend on the manual choice of an appropriate set of features.
Exploiting machine learning methods to extract features from time series
automatically becomes crucial in state-of-the-art time series analysis. In this
paper, we introduce an automated approach to extract time series features based
on time series imaging. We first transform time series into recurrence plots,
from which local features can be extracted using computer vision algorithms.
The extracted features are used for forecast model averaging. Our experiments
show that forecasting based on automatically extracted features, with less
human intervention and a more comprehensive view of the raw time series data,
yields highly comparable performances with the best methods in the largest
forecasting competition dataset (M4) and outperforms the top methods in the
Tourism forecasting competition dataset
Forecasting large collections of time series: feature-based methods
In economics and many other forecasting domains, the real world problems are
too complex for a single model that assumes a specific data generation process.
The forecasting performance of different methods changes depending on the
nature of the time series. When forecasting large collections of time series,
two lines of approaches have been developed using time series features, namely
feature-based model selection and feature-based model combination. This chapter
discusses the state-of-the-art feature-based methods, with reference to
open-source software implementations
Feature-based intermittent demand forecast combinations:accuracy and inventory implications
Intermittent demand forecasting is a ubiquitous and challenging problem in production systems and supply chain management. In recent years, there has been a growing focus on developing forecasting approaches for intermittent demand from academic and practical perspectives. However, limited attention has been given to forecast combination methods, which have achieved competitive performance in forecasting fast-moving time series. The current study examines the empirical outcomes of some existing forecast combination methods and proposes a generalised feature-based framework for intermittent demand forecasting. The proposed framework has been shown to improve the accuracy of point and quantile forecasts based on two real data sets. Further, some analysis of features, forecasting pools and computational efficiency is also provided. The findings indicate the intelligibility and flexibility of the proposed approach in intermittent demand forecasting and offer insights regarding inventory decisions.<br/
Feature-based intermittent demand forecast combinations: bias, accuracy and inventory implications
Intermittent demand forecasting is a ubiquitous and challenging problem in
operations and supply chain management. There has been a growing focus on
developing forecasting approaches for intermittent demand from academic and
practical perspectives in recent years. However, limited attention has been
given to forecast combination methods, which have been proved to achieve
competitive performance in forecasting fast-moving time series. The current
study aims to examine the empirical outcomes of some existing forecast
combination methods, and propose a generalized feature-based framework for
intermittent demand forecasting. We conduct a simulation study to perform a
large-scale comparison of a series of combination methods based on an
intermittent demand classification scheme. Further, a real data set is used to
investigate the forecasting performance and offer insights with regards the
inventory performance of the proposed framework by considering some
complementary error measures. The proposed framework leads to a significant
improvement in forecast accuracy and offers the potential of flexibility and
interpretability in inventory control
Virtualization system for life science automation laboratory
The dissertation developed a Virtualization System to simulate experiment workflows in Life Science Automation (LSA) virtually. The system integrates technologies of process control technology, TCP/IP socket, database, Visual C#, Python Script, Visual Component 3DCreate and 3D modeling, etc. It mainly has four modules: Process Control System, Data Transfer System, Control System and Virtualization Module. The system supplies a vivid and flexible 4D virtualization on LSA experiment workflows for customers, and makes demonstrations for LSA laboratories more conveniently
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