Time series

MQ-ReTCNN: Multi-horizon time series forecasting with retrieval-augmentation

Sitan Yang, Carson Eisenach, Dhruv Madeka

KDD 2022 Workshop on Mining and Learning from Time Series – Deep Forecasting: Models, Interpretability, and Applications

2022

Multi-horizon probabilistic time series forecasting has wide applicability to real-world tasks such as demand forecasting. Recent work in neural time-series forecasting mainly focus on the use of Seq2Seq architectures [25]. For example, MQTransformer [10] – an improvement of MQCNN [27] – has shown the state-of-the-art performance in probabilistic demand forecasting. In this paper, we consider several methods

Machine learning

Identifying and overcoming transformation bias in forecasting models

Sushant More

KDD 2022 Workshop on Mining and Learning from Time Series – Deep Forecasting: Models, Interpretability, and Applications

2022

Log and square root transformations of target variable are routinely used in forecasting models to predict future sales. These transformations often lead to better performing models. However, they also introduce a systematic negative bias (under-forecasting). In this paper, we demonstrate the existence of this bias, dive deep into its root cause and introduce two methods to correct for the bias. We conclude

Machine learning

Representation learning using a multi-branch transformer for industrial time series anomaly detection

Ruichuan Zhang, Fangzhou Cheng, Aparna Pandey

KDD 2022 Workshop on Mining and Learning from Time Series – Deep Forecasting: Models, Interpretability, and Applications

2022

In recent years, due to the rapid expansion of the Industrial Internet of Things (IIoT), substantial amounts of high-dimensional industrial time series data have been generated. Anomaly detection in such industrial time series data is a challenging task due to complex temporal dynamics. In this paper, we propose multi-branch transformer with Gaussian mixture model (MBTGMM), a novel transformer-based framework

Machine learning

Domain adaptation for time series forecasting via attention sharing

Xiaoyong Jin, Youngsuk Park, Danielle Maddix Robinson, Hao Wang, Yuyang (Bernie) Wang

ICML 2022

2022

Recently, deep neural networks have gained increasing popularity in the field of time series forecasting. A primary reason for their success is their ability to effectively capture complex temporal dynamics across multiple related time series. The advantages of these deep forecasters only start to emerge in the presence of a sufficient amount of data. This poses a challenge for typical forecasting problems

Machine learning

Probabilistic continuous-time whole-graph forecasting

Zhihan Gao, Hao Wang, Yuyang (Bernie) Wang, Xingjian Shi, Dit-Yan Yeung

KDD 2022 Workshop on Mining and Learning from Time Series – Deep Forecasting: Models, Interpretability, and Applications

2022

Dynamic graph forecasting has found a wide range of applications including social media, recommendation systems, and computational finance. However, existing dynamic graph models typically focus on discrete-time dynamic graphs, treating dynamic graphs as temporally discrete graph snapshots. We argue that such discrete treatment is inadequate for capturing the underlying dynamics which are intrinsically

Machine learning

A Bayesian model for online activity sample sizes

Thomas S. Richardson, Yu Liu, James McQueen, Doug Hains

AISTATS 2022

2022

In many contexts it is useful to predict the number of individuals in some population who will initiate a particular activity during a given period. For example, the number of users who will install a software update, the number of customers who will use a new feature on a website or who will participate in an A/B test. In practical settings, there is heterogeneity amongst individuals with regard to the

Economics

Bridging recommendation and marketing via recurrent intensity modeling

Yifei Ma, Ge Liu, Anoop Deoras

ICLR 2022

2022

This paper studies some unexplored connections between personalized recommendation and marketing systems. Obviously, the two systems are different, in two main ways. Firstly, personalized item-recommendation (ItemRec) is user-centric, whereas marketing recommends the best user-state segments (UserRec) on behalf of its item providers. (We treat different temporal states of the same user as separate marketing

Machine learning

IB-GAN: A unified approach for multivariate time series classification under class imbalance

Grace Deng, Cuize Han, Tommaso Dreossi, Clarence Lee, David S. Matteson

SDM 2022

2022

Classification of large multivariate time series with strong class imbalance is an important task in real-world applications. Standard methods of class weights, oversampling, or parametric data augmentation do not always yield significant improvements for predicting minority classes of interest. Non-parametric data augmentation with Generative Adversarial Networks (GANs) offers a promising solution. We

Search and information retrieval

Robust probabilistic time series forecasting

TaeHo Yoon, Youngsuk Park, Ernest K. Ryu, Yuyang (Bernie) Wang

AISTATS 2022

2022

Probabilistic time series forecasting has played critical role in decision-making processes due to its capability to quantify uncertainties. Deep forecasting models, however, could be prone to input perturbations, and the notion of such perturbations, together with that of robustness, has not even been completely established in the regime of probabilistic forecasting. In this work, we propose a framework

Machine learning

Multivariate quantile function forecaster

Kelvin Kan, Francois-Xavier Aubet, Tim Januschowski, Youngsuk Park, Konstantinos Benidis, Lars Ruthotto, Jan Gasthaus

AISTATS 2022

2022

We propose Multivariate Quantile Function Forecaster (MQF2), a global probabilistic forecasting method constructed using a multivariate quantile function and investigate its application to multi-horizon forecasting. Prior approaches are either autoregressive, implicitly capturing the dependency structure across time but exhibiting error accumulation with increasing forecast horizons, or multi-horizon sequence-to-sequence

Machine learning

Time series

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