77 citations · 93 across the 4 of their papers we have counts for
6 papers
Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies
Lenon Minorics, Caner Turkmen, David Kernert +3
This paper proposes a new approach for testing Granger non-causality on panel data. Instead of aggregating panel member statistics, we aggregate their corresponding p-values and sh…
Neural Temporal Point Processes: A Review
Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski +1
Temporal point processes (TPP) are probabilistic generative models for continuous-time event sequences. Neural TPPs combine the fundamental ideas from point process literature with…
Intermittent Demand Forecasting with Renewal Processes
Ali Caner Turkmen, Tim Januschowski, Yuyang Wang +1
Intermittency is a common and challenging problem in demand forecasting. We introduce a new, unified framework for building intermittent demand forecasting models, which incorporat…
Intermittent Demand Forecasting with Deep Renewal Processes
Ali Caner Turkmen, Yuyang Wang, Tim Januschowski
Intermittent demand, where demand occurrences appear sporadically in time, is a common and challenging problem in forecasting. In this paper, we first make the connections between…
A Bayesian Choice Model for Eliminating Feedback Loops
Gökhan Çapan, Ilker Gündoğdu, Ali Caner Türkmen +2
Self-reinforcing feedback loops in personalization systems are typically caused by users choosing from a limited set of alternatives presented systematically based on previous choi…
GluonTS: Probabilistic Time Series Models in Python
Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider +10
We introduce Gluon Time Series (GluonTS, available at https://gluon-ts.mxnet.io), a library for deep-learning-based time series modeling. GluonTS simplifies the development of and…