Time series forecasting is essential in many real-world applications, yet developing models that generalize well to unseen and related domains—such as forecasting web traffic on new web sites/platforms or predicting e-commerce demand in new regions—remains a big challenge. Prior work addressing this problem, known as domain generalization, focuses on identifying common patterns but often overlooks the complex characteristics of time series data and fails to use the available information from test samples of unseen domains. We propose a novel approach, adaptive latent decomposition (ALD) for domain generalization in time series forecasting, which consists of a decomposed variational autoencoder (VAE) and an adaptive inference mechanism to improve predictive performance in unseen domains. The decomposed VAE involves a learnable kernel-selection mechanism that learns latent variables of decomposed components of time series, i.e., trend-cyclical and seasonal components. These latent variables capture hidden temporal dependencies in time series data, allowing forecasting models to learn general patterns from various seen domains. The adaptive inference mechanism bridges the gap between seen and unseen domains with a sample-wise optimization strategy specifically designed for time series forecasting. ALD builds a latent variable-aware pre-trained model and tailors it for each test sample, improving the generalization on unseen test domains. We validate ALD across six real-world datasets, from online behavior to complex temporal systems, demonstrating its superior generalization performance compared to state-of-the-art methods.

Human events such as hospital visits, protests, and epidemic outbreaks directly affect individuals, communities, and societies. These events are often influenced by factors such as economics, politics, and public policies of our society. The abundance of online data sources such as social networks, official news articles, and personal blogs chronicle societal events, facilitating the development of AI models for social science, public health care, and decision making. Human event modeling generally comprises both the forecasting stage, which estimates future events based on historical data, and interpretation, which seeks to identify influential factors of such events to understand their causative attributes. Recent achievements, fueled by deep learning and the availability of public data, have significantly advanced the field of human event modeling.

Time series forecasting typically needs to address non-stationary data with evolving trend and seasonal patterns. To address the non-stationarity, reversible instance normalization has been recently proposed to alleviate impacts from the trend with certain statistical measures, e.g., mean and variance. Although they demonstrate improved predictive accuracy, they are limited to expressing basic trends and are incapable of handling seasonal patterns. To address this limitation, this paper proposes a new instance normalization solution, called frequency adaptive normalization (FAN), which extends instance normalization in handling both dynamic trend and seasonal patterns. Specifically, we employ the Fourier transform to identify instance-wise predominant frequent components that cover most non-stationary factors. Furthermore, the discrepancy of those frequency components between inputs and outputs is explicitly modeled as a prediction task with a simple MLP model. FAN is a model-agnostic method that can be applied to arbitrary predictive backbones. We instantiate FAN on four widely used forecasting models as the backbone and evaluate their prediction performance improvements on eight benchmark datasets. FAN demonstrates significant performance advancement, achieving 7.76\%$\sim$37.90\% average improvements in MSE. Our code is publicly available at http://github.com/icannotnamemyself/FAN.

Domain generalization aims to design models that can effectively generalize to unseen target domains by learning from observed source domains. Domain generalization poses a significant challenge for time series data, due to varying data distributions and temporal dependencies. Existing approaches to domain generalization are not designed for time series data, which often results in suboptimal or unstable performance when confronted with diverse temporal patterns and complex data characteristics. We propose a novel approach to tackle the problem of domain generalization in time series forecasting. We focus on a scenario where time series domains share certain common attributes and exhibit no abrupt distribution shifts. Our method revolves around the incorporation of a key regularization term into an existing time series forecasting model: domain discrepancy regularization . In this way, we aim to enforce consistent performance across different domains that exhibit distinct patterns. We calibrate the regularization term by investigating the performance within individual domains and propose the domain discrepancy regularization with domain difficulty awareness . We demonstrate the effectiveness of our method on multiple datasets, including synthetic and real-world time series datasets from diverse domains such as retail, transportation, and finance. Our method is compared against traditional methods, deep learning models, and domain generalization approaches to provide comprehensive insights into its performance. In these experiments, our method showcases superior performance, surpassing both the base model and competing domain generalization models across all datasets. Furthermore, our method is highly general and can be applied to various time series models.

Using observational event data to forecast societal events has been extensively studied in data-driven models. Prior work focuses on correlational analysis and ignores the importance of causal relationships behind events. Understanding the causality of events helps one infer future events by pinpointing potential triggers. In light of complex and dynamic social environments, it is difficult to comprehensively analyze the causes of societal events. In this work, we study the causal relationship between topics and events where topics are extracted from event-related documents. These topics represent probability distributions of words. We introduce a method to discover topics that have a causal effect on future events of interest. Next, we propose a causality-enhanced dynamic heterogeneous graph learning framework where topics, documents, and words are represented as nodes with changing edges. To handle the temporal dependencies of dynamic graphs, we introduce a temporal information learning module that updates node representations based on their evolving context and heterogeneous semantics. We conduct extensive experiments on four real-world datasets and demonstrate the effectiveness of our method in societal event prediction.

Data-driven societal event forecasting methods exploit relevant historical information to predict future events. These methods rely on historical labeled data and cannot accurately predict events when data are limited or of poor quality. Studying causal effects between events goes beyond correlation analysis and can contribute to a more robust prediction of events. However, incorporating causality analysis in data-driven event forecasting is challenging due to several factors: (i) Events occur in a complex and dynamic social environment. Many unobserved variables, i.e., hidden confounders, affect both potential causes and outcomes. (ii) Given spatiotemporal non-independent and identically distributed (non-IID) data, modeling hidden confounders for accurate causal effect estimation is not trivial. In this work, we introduce a deep learning framework that integrates causal effect estimation into event forecasting. We first study the problem of Individual Treatment Effect (ITE) estimation from observational event data with spatiotemporal attributes and present a novel causal inference model to estimate ITEs. We then incorporate the learned event-related causal information into event prediction as prior knowledge. Two robust learning modules, including a feature reweighting module and an approximate constraint loss, are introduced to enable prior knowledge injection. We evaluate the proposed causal inference model on real-world event datasets and validate the effectiveness of proposed robust learning modules in event prediction by feeding learned causal information into different deep learning methods. Experimental results demonstrate the strengths of the proposed causal inference model for ITE estimation in societal events and showcase the beneficial properties of robust learning modules in societal event forecasting.

Deep learning models have been studied to forecast human events using vast volumes of data, yet they still cannot be trusted in certain applications such as healthcare and disaster assistance due to the lack of interpretability. Providing explanations for event predictions not only helps practitioners understand the underlying mechanism of prediction behavior but also enhances the robustness of event analysis. Improving the transparency of event prediction models is challenging given the following factors: (i) multilevel features exist in event data which creates a challenge to cross-utilize different levels of data; (ii) features across different levels and time steps are heterogeneous and dependent; and (iii) static model-level interpretations cannot be easily adapted to event forecasting given the dynamic and temporal characteristics of the data. Recent interpretation methods have proven their capabilities in tasks that deal with graph-structured or relational data. In this paper, we present a Contextualized Multilevel Feature learning framework, CMF, for interpretable temporal event prediction. It consists of a predictor for forecasting events of interest and an explanation module for interpreting model predictions. We design a new context-based feature fusion method to integrate multiple levels of heterogeneous features. We also introduce a temporal explanation module to determine sequences of text and subgraphs that have crucial roles in a prediction. We conduct extensive experiments on several real-world datasets of political and epidemic events. We demonstrate that the proposed method is competitive compared with the state-of-the-art models while possessing favorable interpretation capabilities.

Forecasting influenza-like illness (ILI) is of prime importance to epidemiologists and health-care providers. Early prediction of epidemic outbreaks plays a pivotal role in disease intervention and control. Most existing work has either limited long-term prediction performance or fails to capture spatio-temporal dependencies in data. In this paper, we design a cross-location attention based graph neural network (Cola-GNN) for learning time series embeddings in long-term ILI predictions. We propose a graph message passing framework to combine graph structures (e.g., geolocations) and time-series features (e.g., temporal sequences) in a dynamic propagation process. We compare the proposed method with state-of-the-art statistical approaches and deep learning models. We conducted a set of extensive experiments on real-world epidemic-related datasets from the United States and Japan. The proposed method demonstrated strong predictive performance and leads to interpretable results for long-term epidemic predictions.

Modeling concurrent events of multiple types and their involved actors from open-source social sensors is an important task for many domains such as health care, disaster relief, and financial analysis. Forecasting events in the future can help human analysts better understand global social dynamics and make quick and accurate decisions. Anticipating participants or actors who may be involved in these activities can also help stakeholders to better respond to unexpected events. However, achieving these goals is challenging due to several factors: (i) it is hard to filter relevant information from large-scale input, (ii) the input data is usually high dimensional, unstructured, and Non-IID (Non-independent and identically distributed) and (iii) associated text features are dynamic and vary over time. Recently, graph neural networks have demonstrated strengths in learning complex and relational data. In this paper, we study a temporal graph learning method with heterogeneous data fusion for predicting concurrent events of multiple types and inferring multiple candidate actors simultaneously. In order to capture temporal information from historical data, we propose Glean, a graph learning framework based on event knowledge graphs to incorporate both relational and word contexts. We present a context-aware embedding fusion module to enrich hidden features for event actors. We conducted extensive experiments on multiple real-world datasets and show that the proposed method is competitive against various state-of-the-art methods for social event prediction and also provides much-need interpretation capabilities.

Event forecasting with an aim at modeling contextual information is an important task for applications such as automated analysis generation and resource allocation. Captured contextual information for an event of interest can aid human analysts in understanding the factors associated with that event. However, capturing contextual information within event forecasting is challenging due to several factors: (i) uncertainty of context structure and formulation, (ii) high dimensional features, and (iii) adaptation of features over time. Recently, graph representations have demonstrated success in applications such as traffic forecasting, social influence prediction, and visual question answering systems. In this paper, we study graph representations in modeling social events to identify dynamic properties of event contexts as social indicators. Inspired by graph neural networks, we propose a novel graph convolutional network for predicting future events (e.g., civil unrest movements). We extract and learn graph representations from historical/prior event documents. By employing the hidden word graph features, our proposed model predicts the occurrence of future events and identifies sequences of dynamic graphs as event context. Experimental results on multiple real-world data sets show that the proposed method is competitive against various state-of-the-art methods for social event prediction.