Exploiting big data in time series forecasting: A cross-sectional approach

By Claudio Hartmann, M. Hahmann, Wolfgang Lehner, Frank Rosenthal. 2015

18 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

1. Kasun Bandara
2. Christoph Bergmeir
3. Slawek Smyl
4. Hansika Hewamalage
5. Stefan Zohren
6. Bryan Lim

In Signal Processing

1. Christoph Bergmeir
2. Kasun Bandara
3. Slawek Smyl

Citations in the corpus

• Forecasting Across Time Series Databases using Long Short-Term Memory Networks on Groups of Similar Series (2017, background): “ ETS, ARIMA, Theta, etc., are unable to exercise. To exploit the similarities between related time series, methods to build global models across sets of time series have been introduced. For example, Hartmann et al. (2015) introduce a cross-sectional regression model to sets of related time series o”