Time Series Forecasting Large Language Models

The use of Large Language Models (LLMs) for time series forecasting is a new field that has been gaining attention recently.

Over the past year, I have explored various methods and experiments with LLMs, from using GPT-3 to forecast the price of Bitcoin to employing auto ai agents to predict the next GameStop using insights from Reddit.

However, most of these attempts have failed because the primary function of LLMs is to predict the next word.

But what if we train or fine-tune LLMs specifically for time series forecasting?

I have found 4 different research papers and decided to deep dive into them:

1. TIME-LLM: TIME SERIES FORECASTING BY REPROGRAMMING LARGE LANGUAGE MODELS [29 Jan 2024]

2. MOMENT: A FAMILY OF OPEN TIME-SERIES FOUNDATION MODELS [6 Feb 2024]

3. Lag-Llama: Towards Foundation Models for Time Series Forecasting [20 Nov 2023]

4. TimeGPT [5 Oct 2023]

But first, what is time series forecasting?

Time series forecasting is about using models to look at past data to guess what's going to happen next.

In time series analysis, "seasonality" is a pattern that repeats at regular times, like every month or year. This pattern helps us make better predictions because it gives us a clear structure to follow.

The practical applications of time series forecasting are vast and varied, offering invaluable insights across different sectors.

For businesses, it serves as a strategic tool to anticipate sales trends, thereby facilitating more informed decision-making regarding inventory management.

By accurately predicting future demand, companies can optimize their stock levels, reducing the risk of overstocking or stockouts.

In the retail domain, time series forecasting enabling retailers to prepare for seasonal surges in sales, particularly during the holiday season.

This foresight allows for the strategic planning of marketing campaigns, staffing, and stock levels.

It also offers valuable insights into human behavior. For instance, by tracking and analyzing sleep patterns, researchers can uncover trends and correlations that may influence health and wellness strategies.

This data can then be used to develop personalized recommendations.

So, time series forecasting enabling organizations and individuals to make more informed decisions and strategies based on predictive insights.

Now let's look at the research papers that I mentioned above.

TIME-LLM: Time Series Forecasting by Reprogramming Large Language Models

This paper introduces TIME-LLM, a novel framework that adapts pre-trained large language models (LLMs) for time series forecasting without altering the underlying models.

By reprogramming input time series data into text prototypes and using a Prompt-as-Prefix (PaP) strategy, TIME-LLM aligns the modality between time series and natural language, significantly enhancing the forecasting ability of LLMs.

It showcases an innovative application of LLMs outside their traditional NLP domain, demonstrating superior performance in both few-shot and zero-shot learning scenarios compared to specialized time series forecasting models.

MOMENT: A Family of Open Time-Series Foundation Models:

MOMENT addresses the challenges in pre-training large models for time series analysis due to the lack of a cohesive public repository and the diverse characteristics of time series data.

By compiling a large and varied collection of public time series data and introducing a benchmark for evaluating models in limited supervision settings, MOMENT paves the way for effective large-scale multi-dataset pre-training.

This work contributes significantly to the foundational infrastructure for time series analysis, enabling more effective model evaluation and facilitating advancements in the field.

Lag-Llama: Towards Foundation Models for Time Series Forecasting:

Lag-Llama presents a work-in-progress on developing general-purpose univariate probabilistic time series forecasting models.

By training on a vast collection of time series data, Lag-Llama exhibits promising zero-shot prediction capabilities on unseen, out-of-distribution datasets, outperforming supervised baselines.

The paper highlights the potential of foundation models in time series forecasting and their ability to generalize across different types of time series data, offering insights into the scaling behavior of such models.

TimeGPT-1

TimeGPT-1 introduces the first foundation model for time series forecasting, capable of generating accurate predictions across various unseen datasets.

It outperforms traditional statistical, machine learning, and deep learning methods in zero-shot inference, emphasizing efficiency and simplicity.

The study underscores the applicability of insights from broader AI research to time series analysis, suggesting that large-scale models can democratize access to precise predictions and reduce uncertainty in forecasting tasks.

Overall, these papers collectively advance the understanding and capabilities in time series forecasting, each offering a different approach through which to view the challenges and opportunities in the field.

The models outperforme state-of-the-art forecasting models by large margins, even with the translation process from time series data to textual data with limited vocabulary.

They explore how we can take large language models (LLMs) and tweak them for forecasting, create open-source models for everyone to use, develop tools that can predict trends in general, and prove that big models can make really accurate forecasts.

I've included the papers below.

I'd love to hear your thoughts and what you've learned from them as well!

TIME-LLM: TIME SERIES FORECASTING BY REPROGRAMMING LARGE LANGUAGE MODELS https://arxiv.org/pdf/2310.01728.pdf

MOMENT: A FAMILY OF OPEN TIME-SERIES FOUNDATION MODELS https://arxiv.org/pdf/2402.03885.pdf

Lag-Llama: Towards Foundation Models for Time Series Forecasting https://arxiv.org/pdf/2310.08278.pdf

TimeGPT-1 https://arxiv.org/pdf/2310.03589.pdf