We help marketing and sales teams overcome the disruptive data and technology fragmentation that inhibits insight and forces them to spam their prospects. We do this by injecting Account Intelligence into every step of the buyer journey, wherever our clients interact with customers, and by helping them orchestrate every action across systems and channels - through advertising, account-based experience, and sales motions.

Multivariate time series forecasting has been widely used in various practical scenarios. Recently, Transformer-based models have shown significant potential in forecasting tasks due to the capture of long-range dependencies. However, recent studies in the vision and NLP fields show that the role of attention modules is not clear, which can be replaced by other token aggregation operations. This paper investigates the contributions and deficiencies of attention mechanisms on the performance of time series forecasting. Specifically, we find that (1) attention is not necessary for capturing temporal dependencies, (2) the entanglement and redundancy in the capture of temporal and channel interaction affect the forecasting performance, and (3) it is important to model the mapping between the input and the prediction sequence. To this end, we propose MTS-Mixers, which use two factorized modules to capture temporal and channel dependencies. Experimental results on several real-world datasets show that MTS-Mixers outperform existing Transformer-based models with higher efficiency.

This technology paradigm promises to support innovation and boost employee productivity, and also to power AI, revolutionize how enterprises use data, support business agility, and confront climate change with sustainable solutions. Although new technology and powerful applications are constantly emerging, Lenovo identifies five key components of a future-ready IT environment: smart devices, edge computing, cloud computing, high speed networks such as 5G, and AI. This definition resonates with technical leadership too, says Yuanqing, citing a 2022 Lenovo global research study of 500 chief technology officers in which four out of five CTOs agree it "captures and describes the future of information communications technology (ICT) 'extremely' or'very well.'" Smart devices connect AI to human problems: According to Statista, the number of internet of things (IoT) devices worldwide will reach 29 billion IoT devices by 2030. IoT's exponential growth--smart devices empowered by advanced sensors--provide a wide range of industries with competitive advantages.

The availability of training data remains a significant obstacle for the implementation of machine learning in scientific applications. In particular, estimating how a system might respond to external forcings or perturbations requires specialized labeled data or targeted simulations, which may be computationally intensive to generate at scale. In this study, we propose a novel solution to this challenge by utilizing a principle from statistical physics known as the Fluctuation-Dissipation Theorem (FDT) to discover knowledge using an AI model that can rapidly produce scenarios for different external forcings. By leveraging FDT, we are able to extract information encoded in a large dataset produced by Earth System Models, which includes 8250 years of internal climate fluctuations, to estimate the climate system's response to forcings. Our model, AiBEDO, is capable of capturing the complex, multi-timescale effects of radiation perturbations on global and regional surface climate, allowing for a substantial acceleration of the exploration of the impacts of spatially-heterogenous climate forcers. To demonstrate the utility of AiBEDO, we use the example of a climate intervention technique called Marine Cloud Brightening, with the ultimate goal of optimizing the spatial pattern of cloud brightening to achieve regional climate targets and prevent known climate tipping points. While we showcase the effectiveness of our approach in the context of climate science, it is generally applicable to other scientific disciplines that are limited by the extensive computational demands of domain simulation models. Source code of AiBEDO framework is made available at https://github.com/kramea/kdd_aibedo. A sample dataset is made available at https://doi.org/10.5281/zenodo.7597027. Additional data available upon request.

The performance of time series forecasting has recently been greatly improved by the introduction of transformers. In this paper, we propose a general multi-scale framework that can be applied to the state-of-the-art transformer-based time series forecasting models (FEDformer, Autoformer, etc.). By iteratively refining a forecasted time series at multiple scales with shared weights, introducing architecture adaptations, and a specially-designed normalization scheme, we are able to achieve significant performance improvements, from 5.5% to 38.5% across datasets and transformer architectures, with minimal additional computational overhead. Via detailed ablation studies, we demonstrate the effectiveness of each of our contributions across the architecture and methodology. Furthermore, our experiments on various public datasets demonstrate that the proposed improvements outperform their corresponding baseline counterparts. The essential Figure 1: Intermediate forecasts by our model cross-scale feature relationships are often learnt implicitly, at different time scales. Iterative refinement and are not encouraged by architectural priors of a time series forecast is a strong structural of any kind beyond the stacked attention blocks that prior that benefits time series forecasting. Autoformer (Xu et al., 2021) and Fedformer (Zhou et al., 2022b) introduced some emphasis on scale-awareness by enforcing different computational paths for the trend and seasonal components of the input time series; however, this structural prior only focused on two scales: low-and high-frequency components. Given their importance to forecasting, can we make transformers more scale-aware? We enable this scale-awareness with Scaleformer. In our proposed approach, showcased in Figure 1, time series forecasts are iteratively refined at successive time-steps, allowing the model to better capture the inter-dependencies and specificities of each scale. However, scale itself is not sufficient.

ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information Benedikt Heidrich, Kaleb Phipps, Oliver Neumann, Marian Turowski, Ralf Mikut, Veit Hagenmeyer We combine statistical methods and deep learning-based forecasting methods to enhance probabilistic forecasts. We evaluate ProbPNN empirically on more than 1000 time series from an Electricity and a Traffic data set. On these datasets, the proposed ProbPNN outperforms existing state-of-the-art methods. Abstract Probabilistic forecasts are essential for various downstream applications such as business development, traffic planning, and electrical grid balancing. Many of these probabilistic forecasts are performed on time series data that contain calendar-driven periodicities. However, existing probabilistic forecasting methods do not explicitly take these periodicities into account. Therefore, in the present paper, we introduce a deep learning-based method that considers these calendar-driven periodicities explicitly. The present paper, thus, has a twofold contribution: First, we apply statistical methods that use calendar-driven prior knowledge to create rolling statistics and combine them with neural networks to provide better probabilistic forecasts.

Due to limitations in the computational resources available, General Circulation Models (GCMs) are advocated to simulate the climate system over coarse resolution grids. This hampers the applicability of GCM products in the regional-to-local scale, highly demanded by different socio-economic sectors. Statistical downscaling aims to solve this problem by generating high-resolution climate fields. Recently, machine learning techniques (particularly deep learning models) have shown promising results in this task. These models are first trained in a historical period through observational datasets, and then applied to the GCM outputs of plausible far-future scenarios, thus generating high-resolution climate change products. To assess the plausibility of the derived downscaled fields, several validation frameworks are performed, (e.g., skill to reproduce the present climate) which aim to assess the generalization of the models. Here, we present a novel evaluation protocol building on eXplainable Artificial Intelligence (XAI) to examine the suitability of certain deep learning models for climate downscaling.

Spatio-temporal graph neural networks (STGNN) have become the most popular solution to traffic forecasting. While successful, they rely on the message passing scheme of GNNs to establish spatial dependencies between nodes, and thus inevitably inherit GNNs' notorious inefficiency. Given these facts, in this paper, we propose an embarrassingly simple yet remarkably effective spatio-temporal learning approach, entitled SimST. Specifically, SimST approximates the efficacies of GNNs by two spatial learning techniques, which respectively model local and global spatial correlations. Moreover, SimST can be used alongside various temporal models and involves a tailored training strategy. We conduct experiments on five traffic benchmarks to assess the capability of SimST in terms of efficiency and effectiveness. Empirical results show that SimST improves the prediction throughput by up to 39 times compared to more sophisticated STGNNs while attaining comparable performance, which indicates that GNNs are not the only option for spatial modeling in traffic forecasting.

In this paper, we are interested in developing semantic parsers which understand natural language questions embedded in a conversation with a user and ground them to formal queries over definitions in a general purpose knowledge graph (KG) with very large vocabularies (covering thousands of concept names and relations, and millions of entities). To this end, we develop a dataset where user questions are annotated with Sparql parses and system answers correspond to execution results thereof. We present two different semantic parsing approaches and highlight the challenges of the task: dealing with large vocabularies, modelling conversation context, predicting queries with multiple entities, and generalising to new questions at test time. We hope our dataset will serve as useful testbed for the development of conversational semantic parsers. Our dataset and models are released at https://github.com/EdinburghNLP/SPICE.

The El Ni\~no Southern Oscillation (ENSO) is a semi-periodic fluctuation in sea surface temperature (SST) over the tropical central and eastern Pacific Ocean that influences interannual variability in regional hydrology across the world through long-range dependence or teleconnections. Recent research has demonstrated the value of Deep Learning (DL) methods for improving ENSO prediction as well as Complex Networks (CN) for understanding teleconnections. However, gaps in predictive understanding of ENSO-driven river flows include the black box nature of DL, the use of simple ENSO indices to describe a complex phenomenon and translating DL-based ENSO predictions to river flow predictions. Here we show that eXplainable DL (XDL) methods, based on saliency maps, can extract interpretable predictive information contained in global SST and discover SST information regions and dependence structures relevant for river flows which, in tandem with climate network constructions, enable improved predictive understanding. Our results reveal additional information content in global SST beyond ENSO indices, develop understanding of how SSTs influence river flows, and generate improved river flow prediction, including uncertainty estimation. Observations, reanalysis data, and earth system model simulations are used to demonstrate the value of the XDL-CN based methods for future interannual and decadal scale climate projections.