AiN # 20: Timeseries Forecasting: LLMs for Timeseries.

Foundation Models for TimeSeries: TimeGPT

Welcome to Augmented Intelligence Newsletter (AiN) by C. Naseeb.

AiN Issue # 20

Hey, in this issue, I explain the key concepts around timeseries forecasting centered around Foundation Model TimeGPT.

In the recent issues, I wrote about RAG Pattern , my reflection and highlights for 2023 , and about the five pillars of Trustworthy AI: Transparency , Explainability , Fairness , Robustness , and Privacy.

???????????????????????TimeGPT, Developed by?Nixtla , a foundation model for time series: TimeGPT[1], based on zero-shot inferencing, excels in performance, efficiency, and simplicity. The first pre-trained foundation model for time series forecasting,?"TimeGPT," can produce accurate predictions across various domains and applications without additional training. It is a general pre-trained model that constitutes a groundbreaking innovation that opens the path to a new paradigm for the forecasting practice that is more accessible and accurate, less time-consuming, and drastically reduces computational complexity. The authors claim that TimeGPT is the first foundation model that consistently outperforms alternatives with minimal complexity. Here's all you need to know:

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Transformer-based models have gained popularity in recent years, demonstrating remarkable performance in large-scale settings and complex tasks, such as long-sequence forecasting.

The potential of foundation models, namely large-scale models pre-trained on a large dataset and later fine-tuned for specific tasks, still needs to be explored for time series forecasting tasks. There are, however, early indicators of the possibility of forecasting foundational models. For instance, [Oreshkin et al., 2021] showed that pre-trained models can be transferred between tasks without performance degradation. Additionally, [Kunz et al., 2023] provided evidence of scaling laws on data and model sizes for Transformer architectures on time series forecasting tasks.

Architecture:?It is a Transformer-based time series model with self-attention mechanisms based on [Vaswani et al., 2017]. It takes a window of historical values to produce the forecast, adding local positional encoding to enrich the input. The architecture consists of an?encoder-decoder?structure with multiple layers, each with residual connections and layer normalization. Finally, a linear layer maps the decoder's output to the forecasting window dimension. The general intuition is that attention-based mechanisms can capture the diversity of past events and correctly extrapolate potential future distributions.

Developing a generalized global model for time series entails numerous challenges, primarily due to the complex task of handling signals derived from a broad set of underlying processes. Characteristics such as frequency, sparsity, trend, seasonality, stationarity, and heteroscedasticity present distinct complications for both local and global models. Therefore, any foundational forecasting model must be able to manage such heterogeneity. TimeGPT is engineered to process time series of varied frequencies and characteristics while accommodating different input sizes and forecasting horizons. This adaptability is largely attributable to the underlying transformer-based architecture upon which TimeGPT is built.

??It should be noted that TimeGPT is not based on an existing large language model (LLM). While TimeGPT follows the same principle of training a large transformer model on a vast dataset, its architecture is specialized in handling time series data. It is trained to minimize the forecasting error.

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TimeGPT was trained in the largest publicly available time series collection and can forecast unseen time series without re-training its parameters.

?? By leveraging the most extensive dataset ever – financial, weather, energy, and sales data – TimeGPT brings unparalleled time-series analysis to your terminal! ??????

In the refrences section, I have provided a couple of other papers which discuss the significance of Foundation Models for Tiem Series Data based on multiple domains [2] and [3] mentions a time-series foundation model for forecasting where out-of-the-box zero- shot performance on a variety of public datasets comes close to the accuracy of state-of-the-art supervised forecasting models for each individual dataset. [3] is based on pretraining a patched-decoder style attention model on a large timeseries corpus.

?? You can start using it here -?https://github.com/Nixtla/nixtla

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??Learn all the details in the Research Paper; links are below under the references.

References:

1. TimeGPT1
2. Toward a Foundation Model for Time Series Data
3. A decoder-only foundation model for Time-series Data
4. Github link for TimeGPT

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