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Found 19 papers, 11 papers with code
Probabilistic Forecast Reconciliation with Kullback-Leibler Divergence Regularization
As the popularity of hierarchical point forecast reconciliation methods increases, there is a growing interest in probabilistic forecast reconciliation.
Infinite forecast combinations based on Dirichlet process
Forecast combination integrates information from various sources by consolidating multiple forecast results from the target time series.
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.
Comparison and Evaluation of Methods for a Predict+Optimize Problem in Renewable Energy
As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area.
Forecast combinations: an over 50-year review
Forecast combinations have flourished remarkably in the forecasting community and, in recent years, have become part of the mainstream of forecasting research and activities.
Optimal reconciliation with immutable forecasts
In this paper, we formulate reconciliation methodology that keeps forecasts of a pre-specified subset of variables unchanged or "immutable".
Bayesian forecast combination using time-varying features
In this work, we propose a novel framework for density forecast combination by constructing time-varying weights based on time series features, which is called Feature-based Bayesian Forecasting Model Averaging (FEBAMA).
A hybrid ensemble method with negative correlation learning for regression
It is also possible to improve the NCL ensemble with a regularization term in the objective function.
Exploring the representativeness of the M5 competition data
The main objective of the M5 competition, which focused on forecasting the hierarchical unit sales of Walmart, was to evaluate the accuracy and uncertainty of forecasting methods in the field in order to identify best practices and highlight their practical implications.
Exploring the social influence of Kaggle virtual community on the M5 competition
This paper aims to study the social influence of virtual community on user behaviors in the M5 competition.
Forecast with Forecasts: Diversity Matters
In this work, we suggest a change of focus from the historical data to the produced forecasts to extract features.
Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation
Forecasting models that are trained across sets of many time series, known as Global Forecasting Models (GFM), have shown recently promising results in forecasting competitions and real-world applications, outperforming many state-of-the-art univariate forecasting techniques.
Distributed ARIMA Models for Ultra-long Time Series
Providing forecasts for ultra-long time series plays a vital role in various activities, such as investment decisions, industrial production arrangements, and farm management.
Applications Computation
Déjà vu: forecasting with similarity
Instead of extrapolating, the future paths of the similar reference series are aggregated and serve as the basis for the forecasts of the target series.
Methodology Applications
FFORMPP: Feature-based 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.
Applications
Que será será? The uncertainty estimation of feature-based time series forecasts
In the training part, we use a collection of time series to train a model to explore how time series features affect the interval forecasting accuracy of different forecasting methods, which makes our proposed framework interpretable in terms of the contribution of each feature to the models' uncertainty prediction.
Methodology Applications Computation
Probabilistic Forecasting with Temporal Convolutional Neural Network
We present a probabilistic forecasting framework based on convolutional neural network for multiple related time series forecasting.
Multivariate Time Series Forecasting
Probabilistic Time Series Forecasting
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Forecasting with time series imaging
Recently, the use of time series features for forecast model averaging has been an emerging research focus in the forecasting community.
GRATIS: GeneRAting TIme Series with diverse and controllable characteristics
The explosion of time series data in recent years has brought a flourish of new time series analysis methods, for forecasting, clustering, classification and other tasks.
