Search Results for author:
Found 31 papers, 6 papers with code
Causal Forecasting for Pricing
This paper proposes a novel method for demand forecasting in a pricing context.
Deep Non-Parametric Time Series Forecaster
This paper presents non-parametric baseline models for time series forecasting.
Deep Learning based Forecasting: a case study from the online fashion industry
These include the volume of data, the irregularity, the high amount of turn-over in the catalog and the fixed inventory assumption.
Criteria for Classifying Forecasting Methods
Classifying forecasting methods as being either of a "machine learning" or "statistical" nature has become commonplace in parts of the forecasting literature and community, as exemplified by the M4 competition and the conclusion drawn by the organizers.
On the detrimental effect of invariances in the likelihood for variational inference
Then, we transfer our analysis of the linear model to neural networks.
Intrinsic Anomaly Detection for Multi-Variate Time Series
We introduce a novel, practically relevant variation of the anomaly detection problem in multi-variate time series: intrinsic anomaly detection.
Diverse Counterfactual Explanations for Anomaly Detection in Time Series
In this work we propose a model-agnostic algorithm that generates counterfactual ensemble explanations for time series anomaly detection models.
Resilient Neural Forecasting Systems
Common data challenges are data distribution shifts, missing values and anomalies.
Multivariate Time Series Forecasting with Latent Graph Inference
This paper introduces a new approach for Multivariate Time Series forecasting that jointly infers and leverages relations among time series.
Computational Efficiency
Multivariate Time Series Forecasting
+1
Multivariate Quantile Function Forecaster
We propose Multivariate Quantile Function Forecaster (MQF$^2$), a global probabilistic forecasting method constructed using a multivariate quantile function and investigate its application to multi-horizon forecasting.
Multi-Objective Model Selection for Time Series Forecasting
By learning a mapping from forecasting models to performance metrics, we show that our method PARETOSELECT is able to accurately select models from the Pareto front -- alleviating the need to train or evaluate many forecasting models for model selection.
Online false discovery rate control for anomaly detection in time series
The methods proposed in this article overcome short-comings of previous FDRC rules in the context of anomaly detection, in particular ensuring that power remains high even when the alternative is exceedingly rare (typical in anomaly detection) and the test statistics are serially dependent (typical in time series).
Probabilistic Forecasting: A Level-Set Approach
By recognizing the connection of our algorithm to random forests (RFs) and quantile regression forests (QRFs), we are able to prove consistency guarantees of our approach under mild assumptions on the underlying point estimator.
Meta-Forecasting by combining Global Deep Representations with Local Adaptation
While classical time series forecasting considers individual time series in isolation, recent advances based on deep learning showed that jointly learning from a large pool of related time series can boost the forecasting accuracy.
Deep Explicit Duration Switching Models for Time Series
We propose the Recurrent Explicit Duration Switching Dynamical System (RED-SDS), a flexible model that is capable of identifying both state- and time-dependent switching dynamics.
Neural Flows: Efficient Alternative to Neural ODEs
Neural ordinary differential equations describe how values change in time.
Ranked #3 on
Multivariate Time Series Forecasting
on MIMIC-III
Context-invariant, multi-variate time series representations
Modern time series corpora, in particular those coming from sensor-based data, exhibit characteristics that have so far not been adequately addressed in the literature on representation learning for time series.
A Study of Joint Graph Inference and Forecasting
We study a recent class of models which uses graph neural networks (GNNs) to improve forecasting in multivariate time series.
Detecting Anomalous Event Sequences with Temporal Point Processes
Automatically detecting anomalies in event data can provide substantial value in domains such as healthcare, DevOps, and information security.
Neural Temporal Point Processes: A Review
Temporal point processes (TPP) are probabilistic generative models for continuous-time event sequences.
Normalizing Kalman Filters for Multivariate Time Series Analysis
This paper tackles the modelling of large, complex and multivariate time series panels in a probabilistic setting.
Intermittent Demand Forecasting with Renewal Processes
Intermittency is a common and challenging problem in demand forecasting.
A simple and effective predictive resource scaling heuristic for large-scale cloud applications
We propose a simple yet effective policy for the predictive auto-scaling of horizontally scalable applications running in cloud environments, where compute resources can only be added with a delay, and where the deployment throughput is limited.
Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models
Our method is amenable to streaming anomaly detection and scales to monitoring for anomalies on millions of time series.
The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models
In particular, we investigate the effectiveness of several forms of data binning, i. e. converting real-valued time series into categorical ones, when combined with feed-forward, recurrent neural networks, and convolution-based sequence models.
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
Deep learning based forecasting methods have become the methods of choice in many applications of time series prediction or forecasting often outperforming other approaches.
Intermittent Demand Forecasting with Deep Renewal Processes
Intermittent demand, where demand occurrences appear sporadically in time, is a common and challenging problem in forecasting.
GluonTS: Probabilistic Time Series Models in Python
We introduce Gluon Time Series (GluonTS, available at https://gluon-ts. mxnet. io), a library for deep-learning-based time series modeling.
Deep Factors for Forecasting
We provide both theoretical and empirical evidence for the soundness of our approach through a necessary and sufficient decomposition of exchangeable time series into a global and a local part.
Deep State Space Models for Time Series Forecasting
We present a novel approach to probabilistic time series forecasting that combines state space models with deep learning.
Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale
We present a scalable and robust Bayesian inference method for linear state space models.
