1,831 research outputs found
Dynamic Pooling for the Combination of Forecasts Generated Using Multi Level Learning
In this paper we provide experimental results and
extensions to our previous theoretical findings concerning the
combination of forecasts that have been diversified by three
different methods: with parameters learned at different data
aggregation levels, by thick modeling and by the use of different
forecasting methods. An approach of error variance based
pooling as proposed by Aiolfi and Timmermann has been compared
with flat combinations as well as an alternative pooling
approach in which we consider information about the used
diversification. An advantage of our approach is that it leads to
the generation of novel multi step multi level forecast generation
structures that carry out the combination in different steps of
pooling corresponding to the different types of diversification.
We describe different evolutionary approaches in order to
evolve the order of pooling of the diversification dimensions.
Extensions of such evolutions allow the generation of more
flexible multi level multi step combination structures containing
better adaptive capabilities. We could prove a significant error
reduction comparing results of our generated combination
structures with results generated with the algorithm of Aiolfi
and Timmermann as well as with flat combination for the
application of Revenue Management seasonal forecasting
Review of Nature-Inspired Forecast Combination Techniques
Effective and efficient planning in various areas can be significantly supported by forecasting a variable
like an economy growth rate or product demand numbers for a future point in time. More than one forecast for the same
variable is often available, leading to the question whether one should choose one of the single models or combine
several of them to obtain a forecast with improved accuracy. In the almost 40 years of research in the area of forecast
combination, an impressive amount of work has been done. This paper reviews forecast combination techniques that are
nonlinear and have in some way been inspired by nature
Forecasting and Forecast Combination in Airline Revenue Management Applications
Predicting a variable for a future point in time helps planning for unknown
future situations and is common practice in many areas such as economics, finance,
manufacturing, weather and natural sciences. This paper investigates and compares
approaches to forecasting and forecast combination that can be applied to service
industry in general and to airline industry in particular. Furthermore, possibilities to
include additionally available data like passenger-based information are discussed
Combinations of time series forecasts: when and why are they beneficial?.
Time series forecasting has a long track record in many application areas. In forecasting research, it has been illustrated that finding an individual algorithm that works
best for all possible scenarios is hopeless. Therefore, instead of striving to design a single superior algorithm, current research efforts have shifted towards gaining a
deeper understanding of the reasons a forecasting method may perform well in some conditions whilst it may fail in others. This thesis provides a number of contributions
to this matter. Traditional empirical evaluations are discussed from a novel point of view, questioning the benefit of using sophisticated forecasting methods
without domain knowledge. An own empirical study focusing on relevant off-the shelf forecasting and forecast combination methods underlines the competitiveness
of relatively simple methods in practical applications. Furthermore, meta-features of time series are extracted to automatically find and exploit a link between application
specific data characteristics and forecasting performance using meta-learning. Finally, the approach of extending the set of input forecasts by diversifying functional
approaches, parameter sets and data aggregation level used for learning is discussed, relating characteristics of the resulting forecasts to different error decompositions for
both individual methods and combinations. Advanced combination structures are investigated in order to take advantage of the knowledge on the forecast generation
processes. Forecasting is a crucial factor in airline revenue management; forecasting of the anticipated booking, cancellation and no-show numbers has a direct impact on general planning of routes and schedules, capacity control for fareclasses and overbooking limits. In a collaboration with Lufthansa Systems in Berlin, experiments in the thesis
are conducted on an airline data set with the objective of improving the current net booking forecast by modifying one of its components, the cancellation forecast. To also compare results achieved of the methods investigated here with the current state-of-the-art in forecasting research, some experiments also use data sets of two recent forecasting competitions, thus being able to provide a link between academic research and industrial practice
Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting
Existing relationships among time series can be exploited as inductive biases
in learning effective forecasting models. In hierarchical time series,
relationships among subsets of sequences induce hard constraints (hierarchical
inductive biases) on the predicted values. In this paper, we propose a
graph-based methodology to unify relational and hierarchical inductive biases
in the context of deep learning for time series forecasting. In particular, we
model both types of relationships as dependencies in a pyramidal graph
structure, with each pyramidal layer corresponding to a level of the hierarchy.
By exploiting modern - trainable - graph pooling operators we show that the
hierarchical structure, if not available as a prior, can be learned directly
from data, thus obtaining cluster assignments aligned with the forecasting
objective. A differentiable reconciliation stage is incorporated into the
processing architecture, allowing hierarchical constraints to act both as an
architectural bias as well as a regularization element for predictions.
Simulation results on representative datasets show that the proposed method
compares favorably against the state of the art
A loss discounting framework for model averaging and selection in time series models
We introduce a Loss Discounting Framework for model and forecast combination
which generalises and combines Bayesian model synthesis and generalized Bayes
methodologies. We use a loss function to score the performance of different
models and introduce a multilevel discounting scheme which allows a flexible
specification of the dynamics of the model weights. This novel and simple model
combination approach can be easily applied to large scale model
averaging/selection, can handle unusual features such as sudden regime changes,
and can be tailored to different forecasting problems. We compare our method to
both established methodologies and state of the art methods for a number of
macroeconomic forecasting examples. We find that the proposed method offers an
attractive, computationally efficient alternative to the benchmark
methodologies and often outperforms more complex techniques
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On Model-Selection and Applications of Multilevel Models in Survey and Causal Inference
This thesis includes three parts. The overarching theme is how to analyze multilevel structured datasets, particularly in the areas of survey and causal inference. The first part discusses model selection of hierarchical models, in the context of a national political survey. I found that the commonly used model selection criteria based on predictive accuracy, such as cross validation, don't perform very well in the case of political survey and explore the possible causes. The second part centers around a unique data set on the presidential election collected through an online platform. I show that with adequate modeling, meaningful and highly accurate information could be extracted from this highly-biased data set. The third part builds on a formal causal inference framework for group-structured data, such as meta-analysis and multi-site trials. In particular, I develop a Gaussian Process model under this framework and demonstrate additional insights that can be gained compared with traditional parametric models
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