Neural Temporal Point Processes (TPPs) have emerged as the primary framework
for predicting sequences of events that occur at irregular time intervals, but
their sequential nature can hamper performance for long-horizon forecasts. To
address this, we introduce a novel approach that incorporates a diffusion
generative model. The model facilitates sequence-to-sequence prediction,
allowing multi-step predictions based on historical event sequences. In
contrast to previous approaches, our model directly learns the joint
probability distribution of types and inter-arrival times for multiple events.
This allows us to fully leverage the high dimensional modeling capability of
modern generative models. Our model is composed of two diffusion processes, one
for the time intervals and one for the event types. These processes interact
through their respective denoising functions, which can take as input
intermediate representations from both processes, allowing the model to learn
complex interactions. We demonstrate that our proposal outperforms
state-of-the-art baselines for long-horizon forecasting of TPP.

我们提出了一种使用扩散生成模型的新方法，该模型能够预测基于历史事件序列的多步预测，并学习多个事件类型的类型和时间间隔的联合概率分布，以实现对长时间跨度的预测。

事件序列预测的交互扩散过程

Interacting Diffusion Processes for Event Sequence Forecasting

In the present research, we delve into the intricate realm of
electroencephalogram (EEG) data analysis, focusing on sequence-to-sequence
prediction of data across 32 EEG channels. The study harmoniously fuses the
principles of applied chaos theory and dynamical systems theory to engender a
novel feature set, enriching the representational capacity of our deep learning
model. The endeavour's cornerstone is a transformer-based sequence-to-sequence
architecture, calibrated meticulously to capture the non-linear and
high-dimensional temporal dependencies inherent in EEG sequences. Through
judicious architecture design, parameter initialisation strategies, and
optimisation techniques, we have navigated the intricate balance between
computational expediency and predictive performance. Our model stands as a
vanguard in EEG data sequence prediction, demonstrating remarkable
generalisability and robustness. The findings not only extend our understanding
of EEG data dynamics but also unveil a potent analytical framework that can be
adapted to diverse temporal sequence prediction tasks in neuroscience and
beyond.

研究通过应用混沌理论和动力系统理论，采用基于 Transformer 的序列预测架构，分析了 32 个脑电图通道上的数据，并展现了其在 EEG 数据序列预测方面的强大可靠性和普适性。

人工智能在脑电图预测中的应用：混沌理论

Artificial Intelligence for EEG Prediction: Applied Chaos Theory

In online advertising, the Internet users may be exposed to a sequence of
different ad campaigns, i.e., display ads, search, or referrals from multiple
channels, before led up to any final sales conversion and transaction. For both
campaigners and publishers, it is fundamentally critical to estimate the
contribution from ad campaign touch-points during the customer journey
(conversion funnel) and assign the right credit to the right ad exposure
accordingly. However, the existing research on the multi-touch attribution
problem lacks a principled way of utilizing the users' pre-conversion actions
(i.e., clicks), and quite often fails to model the sequential patterns among
the touch points from a user's behavior data. To make it worse, the current
industry practice is merely employing a set of arbitrary rules as the
attribution model, e.g., the popular last-touch model assigns 100% credit to
the final touch-point regardless of actual attributions. In this paper, we
propose a Dual-attention Recurrent Neural Network (DARNN) for the multi-touch
attribution problem. It learns the attribution values through an attention
mechanism directly from the conversion estimation objective. To achieve this,
we utilize sequence-to-sequence prediction for user clicks, and combine both
post-view and post-click attribution patterns together for the final conversion
estimation. To quantitatively benchmark attribution models, we also propose a
novel yet practical attribution evaluation scheme through the proxy of budget
allocation (under the estimated attributions) over ad channels. The
experimental results on two real datasets demonstrate the significant
performance gains of our attribution model against the state of the art.

本文基于 Dual-attention Recurrent Neural Network 模型，提出了一种关于多点触控补偿，序列预测和预期分配方面的新模型。并且，通过预算分配评估了该模型，证明了其在性能方面优于现有模型。