Understanding Exponential Smoothing Via Kernel Regression

Irène Gijbels · Alun Lloyd Pope · MATTHEW P. WAND

74 citations0/0 files storedJournal of the Royal Statistical Society Series B (Statistical Methodology)

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Forecasting Techniques and Applications Time Series Analysis and Forecasting Statistical and numerical algorithms

Abstract

Summary Exponential smoothing is the most common model-free means of forecasting a future realization of a time series. It requires the specification of a smoothing factor which is usually chosen from the data to minimize the average squared residual of previous one-step-ahead forecasts. In this paper we show that exponential smoothing can be put into a nonparametric regression framework and gain some interesting insights into its performance through this interpretation. We also use theoretical developments from the kernel regression field to derive, for the first time, asymptotic properties of exponential smoothing forecasters.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 74 citations exist upstream.

References

1990Forecasting, Structural Time Series Models and the Kalman Filter 1985Exponential smoothing: The state of the art 1994Forecasting and Time Series: An Applied Approach 1994Forecasting and Time Series: An Applied Approach

Cited by

2014Disentangling systematic and idiosyncratic dynamics in panels of volatility measures 2004Is Garch(1,1) as Good a Model as the Accolades of the Nobel Prize Would Imply?2007Dynamic Integration of Time- and State-Domain Methods for Volatility Estimation 2015Model-Free vs. Model-Based Volatility Prediction 2008Nonparametric Filtering of the Realised Spot Volatility: A Kernel-Based Approach 2022A classification framework and computational methods for human interaction analysis using movement data

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.