9 research outputs found
TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting
Transformers have gained popularity in time series forecasting for their
ability to capture long-sequence interactions. However, their high memory and
computing requirements pose a critical bottleneck for long-term forecasting. To
address this, we propose TSMixer, a lightweight neural architecture exclusively
composed of multi-layer perceptron (MLP) modules. TSMixer is designed for
multivariate forecasting and representation learning on patched time series,
providing an efficient alternative to Transformers. Our model draws inspiration
from the success of MLP-Mixer models in computer vision. We demonstrate the
challenges involved in adapting Vision MLP-Mixer for time series and introduce
empirically validated components to enhance accuracy. This includes a novel
design paradigm of attaching online reconciliation heads to the MLP-Mixer
backbone, for explicitly modeling the time-series properties such as hierarchy
and channel-correlations. We also propose a Hybrid channel modeling approach to
effectively handle noisy channel interactions and generalization across diverse
datasets, a common challenge in existing patch channel-mixing methods.
Additionally, a simple gated attention mechanism is introduced in the backbone
to prioritize important features. By incorporating these lightweight
components, we significantly enhance the learning capability of simple MLP
structures, outperforming complex Transformer models with minimal computing
usage. Moreover, TSMixer's modular design enables compatibility with both
supervised and masked self-supervised learning methods, making it a promising
building block for time-series Foundation Models. TSMixer outperforms
state-of-the-art MLP and Transformer models in forecasting by a considerable
margin of 8-60%. It also outperforms the latest strong benchmarks of
Patch-Transformer models (by 1-2%) with a significant reduction in memory and
runtime (2-3X).Comment: Accepted in the Proceedings of the 29th ACM SIGKDD Conference on
Knowledge Discovery and Data Mining (KDD 23), Research Track. Delayed release
in arXiv to comply with the conference policies on the double-blind review
process. This paper has been submitted to the KDD peer-review process on Feb
02, 202
Hierarchy-guided Model Selection for Time Series Forecasting
Generalizability of time series forecasting models depends on the quality of
model selection. Temporal cross validation (TCV) is a standard technique to
perform model selection in forecasting tasks. TCV sequentially partitions the
training time series into train and validation windows, and performs
hyperparameter optmization (HPO) of the forecast model to select the model with
the best validation performance. Model selection with TCV often leads to poor
test performance when the test data distribution differs from that of the
validation data. We propose a novel model selection method, H-Pro that exploits
the data hierarchy often associated with a time series dataset. Generally, the
aggregated data at the higher levels of the hierarchy show better
predictability and more consistency compared to the bottom-level data which is
more sparse and (sometimes) intermittent. H-Pro performs the HPO of the
lowest-level student model based on the test proxy forecasts obtained from a
set of teacher models at higher levels in the hierarchy. The consistency of the
teachers' proxy forecasts help select better student models at the
lowest-level. We perform extensive empirical studies on multiple datasets to
validate the efficacy of the proposed method. H-Pro along with off-the-shelf
forecasting models outperform existing state-of-the-art forecasting methods
including the winning models of the M5 point-forecasting competition
Automatic leukocyte nucleus segmentation by intuitionistic fuzzy divergence based thresholding
The paper proposes a robust approach to automatic segmentation of leukocyte‟s nucleus from microscopic blood smear images under normal as well as noisy environment by employing a new exponential intuitionistic fuzzy divergence based thresholding technique. The algorithm minimizes the divergence between the actual image and the ideally thresholded image to search for the final threshold. A new divergence formula based on exponential intuitionistic fuzzy entropy has been proposed. Further, to increase its noise handling capacity, a neighborhood-based membership function for the image pixels has been designed. The proposed scheme has been applied on 110 normal and 54 leukemia (chronic myelogenous leukemia) affected blood samples. The nucleus segmentation results have been validated by three expert haematologists. The algorithm achieves an average segmentation accuracy of 98.52% in noise-free environment. It beats the competitor algorithms in terms of several other metrics. The proposed scheme with neighborhood based membership function outperforms the competitor algorithms in terms of segmentation accuracy under noisy environment. It achieves 93.90% and 94.93% accuracies for Speckle and Gaussian noises respectively. The average area under the ROC curves comes out to be 0.9514 in noisy conditions, which proves the robustness of the proposed algorithm
Temporal Dynamics of Workplace Acoustic Scenes: Egocentric Analysis and Prediction
International audienc