Can Large Language Model (LLM) Embeddings Improve Time Series Forecasting?
The integration of large language model (LLM) embeddings into time series forecasting tasks is a growing area of interest. This article explores whether embedding-based features, derived from textual data, can improve the forecasting of financial markets. The discussion is structured around building baseline models, generating embeddings, and comparing performance metrics.
Baseline Forecasting Models Using Traditional Features
A baseline forecasting model often serves as the foundation for comparison with more advanced techniques. In this case, the baseline model includes only traditional time series features, such as lagged values and rolling statistics. These features are engineered to capture temporal dependencies, which are crucial for forecasting future trends.
Lagged features represent a time-shifted version of a variable, allowing the model to understand patterns over a specific period. Similarly, rolling averages and standard deviations smooth out noise and highlight longer-term trends. The dataset is preprocessed to include these features, ensuring that the baseline model effectively captures inherent patterns in the data.
Generating Large Language Model Embeddings
Large language models, such as those trained on extensive textual datasets, generate embeddings that encapsulate semantic information. In this study, embeddings are extracted from financial news headlines. These embeddings serve as engineered features that potentially add contextual richness to the dataset.
Using a pre-trained SentenceTransformer, the text data is transformed into numerical vectors. These vectors are then reduced in dimensionality using techniques like Principal Component Analysis (PCA). The reduced embeddings are appended to the dataset to create a richer feature set for the forecasting model.
Integration of LLM Embeddings into Time Series Data
The integration of LLM embeddings involves combining them with the existing time series features. This requires careful alignment of the textual data with the corresponding time points in the series. For example, daily financial news headlines are aggregated into a single text entry for each day, ensuring temporal consistency.
Once the data is aligned, the embeddings are concatenated with the time series features. This hybrid dataset includes both numerical time series data and semantic information from the textual embeddings, providing a broader context for the forecasting model.
Model Training and Evaluation
The enriched dataset is used to train machine learning models such as LightGBM classifiers. The performance of these models is evaluated using metrics like accuracy, precision, and recall to determine the impact of embedding-based features.
To establish a fair comparison, models trained on the baseline dataset are evaluated alongside those trained on the hybrid dataset. Any observed improvement in predictive performance can be attributed to the inclusion of LLM-generated features.
Performance Comparison and Results
Comparing the performance of models with and without LLM embeddings reveals the practical value of these features. Metrics such as predictive accuracy often show whether the semantic information from textual data contributes to better forecasts.
In the financial domain, even marginal improvements in forecasting accuracy can have significant implications. The results of this study provide insights into the effectiveness of combining textual embeddings with traditional time series features for predictive tasks.
Limitations and Considerations
While the inclusion of LLM embeddings offers potential benefits, it also introduces challenges. These include computational overhead, data alignment complexities, and the risk of overfitting due to high-dimensional features. Addressing these challenges is critical for the successful application of embedding-based approaches.
Furthermore, the study's simplicity in combining textual data could be enhanced through more sophisticated preprocessing techniques, such as filtering for domain-specific relevance or using advanced natural language processing methods.