Forecasting enthusiasts & data scientists exploring the art and science of predicting future trends and outcomes.
Part 2 of a exploratory series on the TimeGPT model from Nixtla. We explore Transformer models and grow our understanding in how they can be applied to time series forecasting.
In the previous post in this series, we covered some of the characteristics of global models for time series forecasting. By training on a varied set of time series data, the model uses the dynamics in those series to create predictions for out-of-sample data. More on that here:
Part 1 of an exploratory series on the TimeGPT model from Nixtla. We'll take a look at how the model works and how it changes the way we think about common forecasting problems.
In this post, we'll explore how the attention mechanism used by the TimeGPT model works and why it makes sense to apply it to time-related tasks.
If you're new to Transformer models and the Attention mechanism, see this post from NVIDIA and these docs from Cohere.
From Section 5.1 Architecture of the paper:
TimeGPT is a Transformer-based time series model with self-attention mechanisms based on [Vaswani et al., 2017]
While originally designed for NLP tasks, transformers have proven to be a versatile architecture with applications beyond language. The success of transformers in capturing long-range dependencies and complex relationships in text has inspired researchers to explore their potential in other domains, such as time series analysis. By adapting the attention mechanism to focus on relevant historical data points, transformers have demonstrated remarkable effectiveness in time series forecasting. This transition from language to time series highlights the adaptability and power of the transformer architecture, opening up new possibilities for tackling a wide range of sequential data problems.
The key to solving complex problems lies in breaking down the domain into its most fundamental concepts or tasks. This process of distillation allows us to convert intricate ideas into manageable, computable problems that algorithms can tackle effectively.
When we look at the fundamental tasks of time series forecasting and language modeling, a similarity emerges: both domains rely on the concept of next-value prediction. In time series forecasting, the goal is to predict the next value in a sequence based on historical data, while in language modeling, the objective is to predict the next word in a sentence based on the preceding words.
Language modeling hadn't always taken this approach, however, the recent success shown by large language models has validated the approach.
In the example below, we get a sense of what the attention mechanism is designed to do. The attention mechanism enables models to selectively focus on relevant parts of the input sequence, enhancing next-word prediction. Instead of traditional sequential processing, attention allows the model to simultaneously consider all previous words, weighing their importance based on relevance to the current prediction. This approach improves the model's ability to capture long-range dependencies and nuanced relationships within the text, resulting in more coherent and contextually appropriate outputs.
By attending to relevant historical data points, a Transformer-based forecasting model can identify patterns and dependencies crucial for predicting future values. This approach allows the model to weigh the importance of different time steps based on their relevance to the prediction task, enabling it to capture complex, non-linear relationships in the data. Transformers can learn to focus on specific periods, such as seasonal patterns or recent trends, while considering the broader context of the time series. This adaptability makes transformers a powerful tool for forecasting tasks across various domains, from financial markets to energy consumption.
The Nixtla team appears to be well aware of the potential pitfalls related to applying transformers to time series. They have marked the author of the popular "Transformers Are What You Do Not Need" post as an acknowledgement on their paper, leading me to believe they've faced the challenges head-on.
Despite the criticisms raised in the article, such as temporal information loss, being outperformed by simpler models, and failing to adapt temporal attention, the Nixtla team has taken steps to address these issues in their TimeGPT model.
By training on billions of diverse time series data points (much more than any of the models highlighted in the post), they aim to capture a wider range of temporal patterns and dynamics.
Additionally, the Nixtla team has incorporated techniques to mitigate the limitations of transformers in time series forecasting. This includes incorporating additional temporal encoding schemes to guide the model's attention.
TimeGPT takes a window of historical values to produce the forecast, adding local positional encoding to enrich the input
While we shouldn't ignore the potential drawbacks of transformers in time series forecasting, the Nixtla team's acknowledgement of these challenges and their extensive training efforts suggest that TimeGPT may have overcome some of these limitations. As with any new model, rigorous evaluation and comparison to existing approaches will be crucial to assess its performance and robustness across various forecasting tasks.
That's all for part two! This post was a little more in-the-weeds, but understanding the architecture that makes this model possible is important.
If you want to see how well TimeGPT performs on your data, you can try it here on Ouro:
Forecasting with TimeGPT from Nixtla, the first generative pre-trained transformer for time series. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT.
Discover assets like this one.