Improving Forecasts for Heterogeneous Time Series by "Averaging", with Application to Food Demand Forecast
A common forecasting setting in real world applications considers a set of possibly heterogeneous time series of the same domain. Due to different properties of each time series such as length, obtaining forecasts for each individual time series in a straight-forward way is challenging. This paper proposes a general framework utilizing a similarity measure in Dynamic Time Warping to find similar time series to build neighborhoods in a k-Nearest Neighbor fashion, and improve forecasts of possibly simple models by averaging. Several ways of performing the averaging are suggested, and theoretical arguments underline the usefulness of averaging for forecasting. Additionally, diagnostics tools are proposed allowing a deep understanding of the procedure.
Code (1)
Tasks
Dynamic Time WarpingTime SeriesSimilar Papers 제목 키워드 기반
Forecasting with a Panel Tobit Model
We use a dynamic panel Tobit model with heteroskedasticity to generate forecasts for a large cross-section of short time series of censored observations. Our fully Bayesian approach allows us to flexibly estimate the cro…
modelTime SeriesTime Series AnalysisDynamic Combination of Heterogeneous Models for Hierarchical Time Series
We introduce a framework to dynamically combine heterogeneous models called \texttt{DYCHEM}, which forecasts a set of time series that are related through an aggregation hierarchy. Different types of forecasting models c…
Time SeriesTime Series AnalysisCombining Forecasts using Meta-Learning: A Comparative Study for Complex Seasonality
In this paper, we investigate meta-learning for combining forecasts generated by models of different types. While typical approaches for combining forecasts involve simple averaging, machine learning techniques enable mo…
Meta-LearningTime SeriesDensity Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective
This paper constructs individual-specific density forecasts for a panel of firms or households using a dynamic linear model with common and heterogeneous coefficients as well as cross-sectional heteroskedasticity. The pa…
Density EstimationTime SeriesTime Series AnalysisMECATS: Mixture-of-Experts for Probabilistic Forecasts of Aggregated Time Series
We introduce a mixture of heterogeneous experts framework called MECATS, which simultaneously forecasts the values of a set of time series that are related through an aggregation hierarchy. Different types of forecasting…
Mixture-of-ExpertsTime SeriesTime Series Analysis