Many practical time series forecasting (TSF) tasks are plagued by data limitations. To alleviate this challenge, we design a data-level augmentation framework. It involves a time series generation (TSG) module and a source data selection (Sel-src) module. TSG aims to achieve better generation results by considering both the global profile and temporal dynamics of series. However, when only few target data is available, TSG module may tend to simulate the limited target samples, leading to poor generalization performance. A natural idea for this problem is to seek help from related source domain, which can provide additional useful information for TSG module. Here we consider a more complex situation, where the relevance between source and target domains is ambiguous. That is, irrelevant samples may exist in the source domain. Blindly using all the source data may lead to counterproductive results. To meet this challenge, Sel-src module is designed to select effective source samples by Inter-Representation Learning (Inter-RL) and Intra-Representation Learning (Intra-RL). Effectiveness of this algorithm is underpinned from two aspects: the quality of the augmented data and the accuracy improvement upon the augmentation.

Time series forecasting (TSF) has gained significant attention as a widely explored research area in diverse applications. Existing methods, which focus on improvements in the most common scenarios, focus little on performance in rare cases. Despite their scarce occurrences in the data, these rare samples are more challenging and easily overlooked by models, significantly contributing to the total loss. In this paper, we propose a novel approach (dubbed iBACon) that overcomes this limitation by employing imbalance-aware contrastive learning and trend-seasonal decomposition architecture, specifically designed to solve TSF. To this end, we first introduce the Input-Output Difference (IOD) metric as a pseudo-label and reveal the data imbalance phenomenon in TSF. This label continuity inherently provides a meaningful distance between targets, implying a similarity between nearby targets in both label and feature spaces. Based on this similarity, the proposed imbalance-aware contrastive loss aims to reshape feature embeddings to facilitate knowledge dissemination among challenging samples and learn specific predictive features. Finally, when combined with our trend-seasonal decomposition network, iBACon significantly improves TSF accuracy. Experiments show that iBACon enhances overall average accuracy and substantially improves the 1-3% most challenging samples.

Train transfer plan ranking has become the core business of online travel platforms (OTPs), due to the flourish development of high- speed rail technology and convenience of booking trains online. Currently, mainstream OTPs adopt rule-based or simple preference- based strategies to rank train transfer plans. However, the insuf- ficient emphasis on the costs of plans and the negligence of con- sidering reference transfer plans make these existing strategies less effective in solving the personalized ranking problem of train transfer plans. To this end, a novel personalized deep network (Plan- Ranker) is presented in this paper to better address the problem. In PlanRanker, a personalized learning component is first proposed to capture both of the query semantics and the target transfer plan- relevant personalized interests of a user over the user's behavior log data. Then, we present a cost learning component, where both of the price cost and the time cost of a target transfer plan are emphasized and learned. Finally, a reference transfer plan learning component is designed to enable the whole framework of PlanRanker to learn from reference transfer plans which are pieced together by plat- form users and thus reflect the wisdom of crowd. PlanRanker is now successfully deployed at Alibaba Fliggy, one of the largest OTPs in China, serving millions of users every day for train ticket reservation. Offline experiments on two production datasets and a country-scale online A/B test at Fliggy both demonstrate the superiority of the proposed PlanRanker over baselines.

In the classical e-commerce platforms, the personalized product-tying recommendation has proven to be of great added value, which improves users' purchase willingness to product-tying by displaying the suitable marketing creative. In this paper, we present a new recommendation problem, i.e., the Pop-up One-time Marketing (POM), where the product-tying marketing creative only pops up one time when the user pays for the main item. POM has become a ubiquitous application in e-commerce platforms, e.g., buy the mobile tying mobile case and buy flight ticket tying insurance. However, many existing recommendation methods are sub-optimal for the creative marketing in the POM scenario due to unconsidering the unique characteristics in the scenario. To tackle this problem, we propose a novel framework named Event-aware Adaptive Clustering Uplift Network (EACU-Net) for the POM scenario, which is to our best knowledge the first attempt along this line. EACU-Net contains three modules: (1) the event-aware graph cascading learning, which employs a heterogeneous graph network to comprehensively learn the embedding for the user attributes, event categories, and creative elements by stage. (2) an adaptive clustering uplift network, which learns the sensitivity of users to creatives under the same context. (3) an event-aware information gain network to learn more information from samples with event affection. Extensive offline and online evaluations on a real-world e-commerce platform demonstrate the superior performance of the proposed model compared with the state-of-the-art method.

Multivariate time series forecasting (MTSF) has gathered extensive attention in various research areas. Many researchers leverage deep neural networks to explore spatial–temporal relationships for MTSF with great success. Nevertheless, plentifully available data required by deep neural networks often struggle to satisfy practical scenarios. To overcome this limitation, we exploit the internal structure of deep neural networks to automatically learn sample relationships, aiming to mitigate data scarcity by propagating information among different data samples. Consequently, in this paper, we propose a M ulti- R elations aware C onvolutional A ttention N etwork, termed MrCAN , which integrates spatial–temporal relation learning and sample relation learning, ad hoc for addressing MTSF issues. In MrCAN, we propose a novel spatial–temporal attention module for spatial–temporal relationship representation learning . With the proposed batch attention module, we further explore relational modeling among samples in each mini-batch to implicitly enrich the training sample information. In particular, we introduce parameter-sharing regressors located before and after the batch attention module to alleviate the training and testing inconsistency in learning batch invariant representations . Extensive experiments on six practical datasets demonstrate that MrCAN compares favorably to nine baseline models . Larger performance gaps are exhibited, especially on small datasets like the SML2010 dataset. Code is publicly available at https://github.com/JZhangNA/MrCAN .