In recent times, the field of unsupervised representation learning (URL) for
time series data has garnered significant interest due to its remarkable
adaptability across diverse downstream applications. Unsupervised learning
goals differ from downstream tasks, making it tricky to ensure downstream task
utility by focusing only on temporal feature characterization. Researchers have
proposed multiple transformations to extract discriminative patterns implied in
informative time series, trying to fill the gap. Despite the introduction of a
variety of feature engineering techniques, e.g. spectral domain, wavelet
transformed features, features in image form and symbolic features etc. the
utilization of intricate feature fusion methods and dependence on heterogeneous
features during inference hampers the scalability of the solutions. To address
this, our study introduces an innovative approach that focuses on aligning and
binding time series representations encoded from different modalities, inspired
by spectral graph theory, thereby guiding the neural encoder to uncover latent
pattern associations among these multi-modal features. In contrast to
conventional methods that fuse features from multiple modalities, our proposed
approach simplifies the neural architecture by retaining a single time series
encoder, consequently leading to preserved scalability. We further demonstrate
and prove mechanisms for the encoder to maintain better inductive bias. In our
experimental evaluation, we validated the proposed method on a diverse set of
time series datasets from various domains. Our approach outperforms existing
state-of-the-art URL methods across diverse downstream tasks.

我们的研究提出了一种创新方法，通过对齐和绑定不同模态的时间序列表示，以引导神经编码器揭示这些多模态特征之间的潜在模式关联，从而解决了由于复杂的特征融合方法和对异质特征的依赖导致的扩展性问题，并在多个应用领域的不同时间序列数据集上验证了该方法的有效性。

无监督多模态特征对齐的时间序列表示学习

Unsupervised Multi-modal Feature Alignment for Time Series  Representation Learning

The ACM RecSys Challenge 2023, organized by ShareChat, aims to predict the
probability of the app being installed. This paper describes the lightweight
solution to this challenge. We formulate the task as a user response prediction
task. For rapid prototyping for the task, we propose a lightweight solution
including the following steps: 1) using adversarial validation, we effectively
eliminate uninformative features from a dataset; 2) to address noisy continuous
features and categorical features with a large number of unique values, we
employ feature engineering techniques.; 3) we leverage Gradient Boosted
Decision Trees (GBDT) for their exceptional performance and scalability. The
experiments show that a single LightGBM model, without additional ensembling,
performs quite well. Our team achieved ninth place in the challenge with the
final leaderboard score of 6.059065. Code for our approach can be found here:
this https URL

本文描述了 ACM RecSys Challenge 2023 的轻量级解决方案，将任务形式化为用户响应预测任务，并通过敌对验证、特征工程技术和梯度提升决策树等方法有效地解决了问题。实验证明单个 LightGBM 模型表现出色，无需额外集成。