FRANS: Automatic Feature Extraction for Time Series Forecasting

• URL: http://arxiv.org/abs/2209.07018v1
• Date: Thu, 15 Sep 2022 03:14:59 GMT
• Title: FRANS: Automatic Feature Extraction for Time Series Forecasting
• Authors: Alexey Chernikov, Chang Wei Tan, Pablo Montero-Manso, Christoph Bergmeir
• Abstract summary: We develop an autonomous Feature Retrieving Autoregressive Network for Static features that does not require domain knowledge. Our results show that our features lead to improvement in accuracy in most situations.
• Score: 2.3226893628361682
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Feature extraction methods help in dimensionality reduction and capture relevant information. In time series forecasting (TSF), features can be used as auxiliary information to achieve better accuracy. Traditionally, features used in TSF are handcrafted, which requires domain knowledge and significant data-engineering work. In this research, we first introduce a notion of static and dynamic features, which then enables us to develop our autonomous Feature Retrieving Autoregressive Network for Static features (FRANS) that does not require domain knowledge. The method is based on a CNN classifier that is trained to create for each series a collective and unique class representation either from parts of the series or, if class labels are available, from a set of series of the same class. It allows to discriminate series with similar behaviour but from different classes and makes the features extracted from the classifier to be maximally discriminatory. We explore the interpretability of our features, and evaluate the prediction capabilities of the method within the forecasting meta-learning environment FFORMA. Our results show that our features lead to improvement in accuracy in most situations. Once trained our approach creates features orders of magnitude faster than statistical methods.

Related papers

• An Additive Instance-Wise Approach to Multi-class Model Interpretation [53.87578024052922]
  Interpretable machine learning offers insights into what factors drive a certain prediction of a black-box system. Existing methods mainly focus on selecting explanatory input features, which follow either locally additive or instance-wise approaches. This work exploits the strengths of both methods and proposes a global framework for learning local explanations simultaneously for multiple target classes.
  arXiv  Detail & Related papers  (2022-07-07T06:50:27Z)
• Exploring Category-correlated Feature for Few-shot Image Classification [27.13708881431794]
  We present a simple yet effective feature rectification method by exploring the category correlation between novel and base classes as the prior knowledge. The proposed approach consistently obtains considerable performance gains on three widely used benchmarks.
  arXiv  Detail & Related papers  (2021-12-14T08:25:24Z)
• Learning Debiased and Disentangled Representations for Semantic Segmentation [52.35766945827972]
  We propose a model-agnostic and training scheme for semantic segmentation. By randomly eliminating certain class information in each training iteration, we effectively reduce feature dependencies among classes. Models trained with our approach demonstrate strong results on multiple semantic segmentation benchmarks.
  arXiv  Detail & Related papers  (2021-10-31T16:15:09Z)
• Temporal Dependencies in Feature Importance for Time Series Predictions [4.082348823209183]
  We propose WinIT, a framework for evaluating feature importance in time series prediction settings. We demonstrate how the solution improves the appropriate attribution of features within time steps. WinIT achieves 2.47x better performance than FIT and other feature importance methods on real-world clinical MIMIC-mortality task.
  arXiv  Detail & Related papers  (2021-07-29T20:31:03Z)
• Dynamic Instance-Wise Classification in Correlated Feature Spaces [15.351282873821935]
  In a typical machine learning setting, the predictions on all test instances are based on a common subset of features discovered during model training. A new method is proposed that sequentially selects the best feature to evaluate for each test instance individually, and stops the selection process to make a prediction once it determines that no further improvement can be achieved with respect to classification accuracy. The effectiveness, generalizability, and scalability of the proposed method is illustrated on a variety of real-world datasets from diverse application domains.
  arXiv  Detail & Related papers  (2021-06-08T20:20:36Z)
• Few-Shot Incremental Learning with Continually Evolved Classifiers [46.278573301326276]
  Few-shot class-incremental learning (FSCIL) aims to design machine learning algorithms that can continually learn new concepts from a few data points. The difficulty lies in that limited data from new classes not only lead to significant overfitting issues but also exacerbate the notorious catastrophic forgetting problems. We propose a Continually Evolved CIF ( CEC) that employs a graph model to propagate context information between classifiers for adaptation.
  arXiv  Detail & Related papers  (2021-04-07T10:54:51Z)
• Learning summary features of time series for likelihood free inference [93.08098361687722]
  We present a data-driven strategy for automatically learning summary features from time series data. Our results indicate that learning summary features from data can compete and even outperform LFI methods based on hand-crafted values.
  arXiv  Detail & Related papers  (2020-12-04T19:21:37Z)
• Self-Challenging Improves Cross-Domain Generalization [81.99554996975372]
  Convolutional Neural Networks (CNN) conduct image classification by activating dominant features that correlated with labels. We introduce a simple training, Self-Challenging Representation (RSC), that significantly improves the generalization of CNN to the out-of-domain data. RSC iteratively challenges the dominant features activated on the training data, and forces the network to activate remaining features that correlates with labels.
  arXiv  Detail & Related papers  (2020-07-05T21:42:26Z)
• Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification [91.67977602992657]
  We propose a new strategy based on feature selection, which is both simpler and more effective than previous feature adaptation approaches. We show that a simple non-parametric classifier built on top of such features produces high accuracy and generalizes to domains never seen during training.
  arXiv  Detail & Related papers  (2020-03-20T15:44:17Z)
• Learning Class Regularized Features for Action Recognition [68.90994813947405]
  We introduce a novel method named Class Regularization that performs class-based regularization of layer activations. We show that using Class Regularization blocks in state-of-the-art CNN architectures for action recognition leads to systematic improvement gains of 1.8%, 1.2% and 1.4% on the Kinetics, UCF-101 and HMDB-51 datasets, respectively.
  arXiv  Detail & Related papers  (2020-02-07T07:27:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.