Diffusion Auto-Regressive Transformer For Effective Self-Supervised Time Series Forecasting

Author(s): Reza Yazdanfar

Originally published on Towards AI.

Time series is important (though underappreciated!) in various domains due to its ability to provide accurate predictions of future data points, which in turn leads to better decision-making, resource allocation, and risk management. This capability leads to significant operational improvements and strategic advantages, particularly in fields such as finance, healthcare, and energy management.

Deep neural networks have emerged as a popular and effective solution paradigm for time series forecasting, reflecting the growing interest in leveraging advanced machine learning techniques to tackle the complexities of sequential data.

Self-supervised Learning

A paradigm that models learn from unlabeled data by generating supervisory signals internally, typically through pretext tasks.

Unlike supervised learning, which requires labeled data, self-supervised learning leverages the inherent structure within the data to create the necessary labels for training.

Self-supervised Learning for Time Series:

In the context of self-supervised learning, time series offers unique abilities to develop models that can learn universal representations from unlabeled data.

This approach enhances time series forecasting by allowing models to capture both long-term dependencies and local detail features. However, effectively capturing these aspects remains challenging, prompting the need for innovative methods like TimeDART (this paper), which integrates diffusion and auto-regressive modeling to address these challenges.

Problem:

The challenge for time series is capturing global sequence dependencies and local detail features effectively using self-supervised learning methods.

Traditional methods struggle with this dual task, impacting their ability to learn comprehensive and expressive representations of time series data.

The solution is TimeDarT:

TimeDarT

In one word, it’s the “solution“ to time series forecasting problem! but well, that’s not enough! We gotta inspect and dig into it 🙂

TimeDART, short for Diffusion Auto-regressive Transformer, is a self-supervised learning method designed for time series forecasting. It aims to improve the prediction of future data points by learning from patterns in past data within a time series. It’s like breaking down the time series data into smaller segments, patches, and uses these patches as the basic units for modeling.

The researchers used Transformer encoder with self-attention mechanisms to understand dependencies between these patches, effectively capturing the overall sequence structure of the data.

Two processes of diffusion and denoising are used to address detailed features within each patch. These two help to capture local features by adding and removing noises from data (pretty typical process in all diffusion models). As a matter of fact, it helps the model to behave better on detailed patterns.

TimeDART Architecture:

Instance Normalization and Patching Embedding

The first step is applying instance normalization (normalization) to the input multivariate time series data to ensure each instance has a zero mean and unit standard deviation, which helps maintain consistency in the final forecast.

The time series data is divided into patches instead of individual points, this allows capturing more comprehensive local information.

The patch length is set equal to the stride to avoid information leakage, this helps us to make sure each patch contains only non-overlapping segments of the original sequence.

Transformer Encoder for Inter-Patch Dependencies

We have a self-attention-based Transformer encoder in the architecture, this helps to model the dependencies between patches.

This approach helps in capturing the global sequence dependencies by considering the relationships between different patches of the time series data.

The use of a Transformer encoder allows TimeDART to learn meaningful inter-patch representations, which are crucial for understanding the high-level structure of the time series.

Forward Diffusion Process

In the forward diffusion process, noise is applied on the input patches. This step is essential for generating self-supervised signals that enable the model to learn robust representations by reconstructing the original data from its noisy version.

This noise helps the model recognize and focus on the intrinsic patterns within the time series data.

Cross-Attention-Based Denoising Decoder

The denoising decoder employs a cross-attention mechanism to reconstruct the original, noise-free patches.

This allows for adjustable optimization difficulty, making the self-supervised task more effective and enabling the model to focus on capturing detailed intra-patch features. This design increases the model’s capability to learn both local and global features effectively.

It takes the noises (as query) and the outputs of the encoder (keys and values) in, and we mask the decoder to make sure that the j-th input in noise added corresponds to the j-th output from the Transformer encoder.

Auto-Regressive Generation for Global Dependencies

The responsibility is capturing the high-level global dependencies in the time series. By restoring the original sequence auto-regressively, the model can understand the overall temporal patterns and dependencies, improving its forecasting ability.

Optimization and Fine-Tuning

Finally, the entire model is optimized in an auto-regressive manner to obtain transferable representations that can be fine-tuned for specific forecasting tasks. This step ensures that the model’s learned representations are both comprehensive and adaptable to various downstream applications, enabling superior performance in time series forecasting.

Evaluation:

Datasets

The TimeDART model was evaluated using eight popular datasets to test its effectiveness in time series forecasting. These datasets include four ETT datasets (ETTh1, ETTh2, ETTm1, ETTm2), as well as the Weather, Exchange, Electricity, and Traffic datasets.

These datasets include a range of application scenarios, such as power systems, transportation networks, and weather forecasting. (as I said, time series is important everywhere 👀🙂)

Results

Please note that the researchers mentioned more details of the work such as hyper parameters; however, aiming to prevent this article longer I did not mention them and herby refer you to read the original paper.

You can also contact me directly via LinkedIn or X platform (formerly twitter)🙂🔥🤗

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Published via Towards AI