Multivariate Time Series (MTS) analysis is crucial to understanding and managing complex systems, such as traffic and energy systems, and a variety of approaches to MTS forecasting have been proposed recently. However, we often observe inconsistent or seemingly contradictory performance findings across different studies. This hinders our understanding of the merits of different approaches and slows down progress. We address the need for means of assessing MTS forecasting proposals reliably and fairly, in turn enabling better exploitation of MTS as seen in different applications. Specifically, we first propose BasicTS+, a benchmark designed to enable fair, comprehensive, and reproducible comparison of MTS forecasting solutions. BasicTS+ establishes a unified training pipeline and reasonable settings, enabling an unbiased evaluation. Second, we identify the heterogeneity across different MTS as an important consideration and enable classification of MTS based on their temporal and spatial characteristics. Disregarding this heterogeneity is a prime reason for difficulties in selecting the most promising technical directions. Third, we apply BasicTS+ along with rich datasets to assess the capabilities of more than 30 MTS forecasting solutions. This provides readers with an overall picture of the cutting-edge research on MTS forecasting.

Temporal data, representing chronological observations of complex systems, has always been a typical data structure that can be widely generated by many domains, such as industry, finance, healthcare, and climatology. Analyzing the underlying structures, i.e., the causal relations, could be extremely valuable for various applications. Recently, causal discovery from temporal data has been considered as an interesting yet critical task and attracted much research attention. According to the nature and structure of temporal data, existing causal discovery works can be divided into two highly correlated categories i.e., multivariate time series causal discovery, and event sequence causal discovery. However, most previous surveys are only focused on the multivariate time series causal discovery but ignore the second category. In this article, we specify the similarity between the two categories and provide an overview of existing solutions. Furthermore, we provide public datasets, evaluation metrics, and new perspectives for temporal data causal discovery.

Temporal data representing chronological observations of complex systems can be ubiquitously collected in smart industry, medicine, finance and etc. In the last decade, many tasks have been studied for mining temporal data and offered significant value for various applications. Among these tasks, causal discovery aims to understand the underlying generation mechanism of temporal data and has attracted much research attention. According to whether the data is calibrated, existing causal discovery approaches can be divided into two subtasks, i.e., multivariate time-series causal discovery, and event sequence causal discovery. Previous tutorials or surveys have primarily focused on causal discovery from time-series data and disregarded the second ones. In this tutorial, we elucidate the correlation between the two subtasks and provide a comprehensive review of the existing solutions. Moreover, we offer some potential applications and summarize new perspectives for discovering causal relations from temporal data. We hope the audiences can obtain a systematic overview of this topic and inspire some new ideas for their own research.

Time series similarity computation is a fundamental primitive that underpins many time series data analysis tasks. However, many existing time series similarity measures have a high computation cost. While there has been much research effort for reducing the computational cost, such effort is usually specific to one similarity measure. We propose NEUTS (Neural metric learning for Time Series) to accelerate time series similarity computation in a generic fashion. NEUTS computes the similarity of a given time series pair in linear time and generic to handle any existing similarity measures. NEUTS samples a number of seed time series from the given database, and then uses their pair-wise similarities as guidance to approximate the similarity function with a neural metric learning framework. NEUTS features two novel modules to achieve accurate approximation of the similarity function: (1) a local attention memory module that augments existing recurrent neural networks for time series encoding; and (2) a distance-weighted ranking loss that effectively transcribes information from the seed-based guidance. With these two modules, NEUTS can yield high accuracies and fast convergence rates even if the training data is small. Our experiments with five real-life datasets and four similarity measures (Fréchet, Hausdorff, ERP and DTW) show that NEUTS outperforms baselines consistently and significantly. Specifically, it achieves over 80% accuracies in most settings, while obtaining 50x-1000x speedup over bruteforce methods and 3x-350x speedup over approximate algorithms for top-k similarity search.

Multi-touch attribution (MTA), aiming to estimate the contribution of each advertisement touchpoint in conversion journeys, is essential for budget allocation and automatically advertising. Existing methods first train a model to predict the conversion probability of the advertisement journeys with historical data and calculate the attribution of each touchpoint by using the results counterfactual predictions. An assumption of these works is the conversion prediction model is unbiased. It can give accurate predictions on any randomly assigned journey, including both the factual and counterfactual ones. Nevertheless, this assumption does not always hold as the user preferences act as the common cause for both ad generation and user conversion, involving the confounding bias and leading to an out-of-distribution (OOD) problem in the counterfactual prediction. In this paper, we define the causal MTA task and propose CausalMTA to solve this problem. It systemically eliminates the confounding bias from both static and dynamic perspectives and learn an unbiased conversion prediction model using historical data. We also provide a theoretical analysis to prove the effectiveness of CausalMTA with sufficient ad journeys. Extensive experiments on both synthetic and real data in Alibaba advertising platform show that CausalMTA can not only achieve better prediction performance than the state-of-the-art method but also generate meaningful attribution credits across different advertising channels.