Related references
Note: Only part of the references are listed.A multi-layer and multi-ensemble stock trader using deep learning and deep reinforcement learning
Salvatore Carta et al.
APPLIED INTELLIGENCE (2021)
Hierarchical forecast reconciliation with machine learning
Evangelos Spiliotis et al.
APPLIED SOFT COMPUTING (2021)
Understanding forecast reconciliation
Ross Hollyman et al.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2021)
Implementing transfer learning across different datasets for time series forecasting
Rui Ye et al.
PATTERN RECOGNITION (2021)
Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption
Evangelos Spiliotis et al.
APPLIED ENERGY (2020)
Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach
Kasun Bandara et al.
EXPERT SYSTEMS WITH APPLICATIONS (2020)
DSTP-RNN: A dual-stage two-phase attention-based recurrent neural network for long-term and multivariate time series prediction
Yeqi Liu et al.
EXPERT SYSTEMS WITH APPLICATIONS (2020)
Hierarchical demand forecasting benchmark for the distribution grid
Lorenzo Nespoli et al.
ELECTRIC POWER SYSTEMS RESEARCH (2020)
Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization
Shanika L. Wickramasuriya et al.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2019)
Deep learning for time series classification: a review
Hassan Ismail Fawaz et al.
DATA MINING AND KNOWLEDGE DISCOVERY (2019)
Cross-temporal coherent forecasts for Australian tourism
Nikolaos Kourentzes et al.
ANNALS OF TOURISM RESEARCH (2019)
Time series analysis with explanatory variables: A systematic literature review
Paula Medina Macaira et al.
ENVIRONMENTAL MODELLING & SOFTWARE (2018)
Designing architectures of convolutional neural networks to solve practical problems
Martha Dais Ferreira et al.
EXPERT SYSTEMS WITH APPLICATIONS (2018)
Cluster-based hierarchical demand forecasting for perishable goods
Jakob Huber et al.
EXPERT SYSTEMS WITH APPLICATIONS (2017)
Detecting anomalies in time series data via a deep learning algorithm combining wavelets, neural networks and Hilbert transform
Stratis Kanarachos et al.
EXPERT SYSTEMS WITH APPLICATIONS (2017)
Determining an optimal hierarchical forecasting model based on the characteristics of the data set: Technical note
Zlatana D. Nenova et al.
JOURNAL OF OPERATIONS MANAGEMENT (2016)
Optimal combination forecasts for hierarchical time series
Rob J. Hyndman et al.
COMPUTATIONAL STATISTICS & DATA ANALYSIS (2011)
Combining SKU-level sales forecasts from models and experts
Philip Hans Franses et al.
EXPERT SYSTEMS WITH APPLICATIONS (2011)
Hierarchical forecasts for Australian domestic tourism
George Athanasopoulos et al.
INTERNATIONAL JOURNAL OF FORECASTING (2009)
The M3 competition: Statistical tests of the results
AJ Koning et al.
INTERNATIONAL JOURNAL OF FORECASTING (2005)