Probabilistic Demand Forecasting at Scale

By Joos-Hendrik Böse, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Dustin Lange, David Salinas, Sebastian Schelter, Matthias W. Seeger, Bernie Wang. 2017

158 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Probabilistic & Bayesian Methods, Signal Processing, Time-Series & Forecasting

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Citations in the corpus

Time-series forecasting with deep learning: a survey (2020, background): “Time series modelling has historically been a key area of academic research – forming an integral part of applications in topics such as climate modelling [1], biological sciences [2] and medicine [3], as well as commercial decision making in retail [4] and ﬁnance [5] to name a few.”
Deep Factors for Forecasting (2019, methodology): “…in extracting complex patterns from large datasets of related time series by exploiting similar smoothness and temporal dynamics, and common responses to exogenous input, i.e. dependencies of type (1) and (3) (Flunkert et al., 2017; Wen et al., 2017; Mukherjee et al., 2018; Gasthaus et al., 2019).”
One Fits All: Power General Time Series Analysis by Pretrained LM (2023, background): “…& Athanasopoulos (2021) that has played an important role in many real-world applications Wen et al. (2022), such as retail sales forecasting Böse et al. (2017); Courty & Li (1999) , imputation of missing data for economic time series Friedman (1962) anomaly detection for industrial…”
Data Lifecycle Challenges in Production Machine Learning (2018, background): “In this article, driven from our experience in building data management infrastructure for a large-scale ML platform [11], we identify several core challenges in the management of ML data that are relevant to [11] and other ML platforms [17].”
Automating Large-Scale Data Quality Verification (2018, background): “Online retailers, for example, rely on data to support customers making buying decisions, to forecast demand [7], to schedule deliveries, and more generally, to guide every single business process and decision.”
Forecasting Big Time Series: Old and New (2018, methodology): “We conclude the tutorial with a summary of the previous parts and share the lessons learned building the scalable forecasting system for retail and cloud resource within Amazon [1] and developing cloud-based deep learning forecasting algorithms such as DeepAR [6] in AWS SageMaker [13].”
Sample Path Generation for Probabilistic Demand Forecasting (2018, methodology): “Recent methods, such as [4], [2], [14] utilize artificial neural networks such as Feed-Forward Networks or Recurrent Neural Networks (RNN) [11], along with likelihood based loss functions to produce”
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey (2020, background): “It has many important applications [54] such as forecasting the demand for items sold by retailers [41, 190, 156, 136, 14, 25], the flow of traffic [111, 126, 118], the demand and supply of energy [45, 170, 117, 157], or the covariance matrix, volatility and long-tail distributions in finance [30, 2”
TSMixer: An all-MLP Architecture for Time Series Forecasting (2023, background): “Time series forecasting is a common problem in many real-world use cases – including forecasting product demand (Böse et al., 2017; Courty & Li, 1999), pandemics spread (Zhang & Nawata, 2018), and inflation rates (Capistrán et al.”