Foundation Models for Time Series: A New Era in Financial Forecasting
Introduction: From Big Data to Big Models in Finance
Imagine an AI that has learned from every kind of time series data – from stock prices and economic indicators to energy usage and heartbeats – and can leverage that knowledge to forecast market trends or detect signals with minimal retraining. This is the promise of foundation models for time series, a frontier in AI that is poised to transform how quants and financial institutions approach forecasting and trend analysis. In recent years, large foundation models (pre-trained on vast data via self-supervised learning) have revolutionized natural language processing and computer vision ([2310.03916] Toward a Foundation Model for Time Series Data). Now, similar AI for financial forecasting is emerging in the time series domain, aiming to bring GPT-like capabilities to financial data analysis. For financial product teams and institutional investors, this development could mean faster model development, more accurate financial trend prediction, and the ability to uncover subtle market signals that were previously hard to detect.
([2310.03916] Toward a Foundation Model for Time Series Data) Figure: Concept of a time series foundation model. A single “foundation” model is pre-trained on a large, diverse set of time series (e.g. power consumption, heartbeat, sales data, etc.) from multiple domains. This pre-trained model can then be fine-tuned on specific smaller datasets (such as a particular financial asset’s history) to perform downstream tasks like prediction or classification. The broad pretraining provides a strong starting point, so each fine-tuned model learns faster and often achieves better accuracy than training from scratch (Toward a Foundation Model for Time Series Data).
What is a Time Series Foundation Model?
A time series foundation model is essentially a large pre-trained model for sequential data. Instead of training a separate model for each forecasting task or each dataset, a foundation model learns general patterns from a massive multi-domain dataset and then adapts to many tasks ([2310.03916] Toward a Foundation Model for Time Series Data) ([2310.03916] Toward a Foundation Model for Time Series Data). Yeh et al.’s research paper “Toward a Foundation Model for Time Series Data” ([2310.03916] Toward a Foundation Model for Time Series Data) provides a clear definition: it’s a model trained on large and diverse time series via self-supervised pre-training, which can be fine-tuned for various downstream uses ([2310.03916] Toward a Foundation Model for Time Series Data). In other words, it serves as a universal base model for time series, analogous to how GPT-3 is a base model for language.
Key characteristics of such models include:
Scale and Diversity: They are trained on extremely large datasets spanning many domains (finance, healthcare, energy, etc.), capturing a wide range of temporal patterns. For example, the UCR archive used by Yeh et al. includes 128 different time series datasets ranging from ECG heartbeats to power consumption ([2310.03916] Toward a Foundation Model for Time Series Data). Recent industry efforts go even further – e.g., Google’s TimesFM model was pre-trained on 100 billion time points from countless real-world series (A decoder-only foundation model for time-series forecasting), and Salesforce’s Moirai learned from 27 billion observations across nine domains (Moirai: A Time Series Foundation Model for Universal Forecasting).
Self-Supervised Pretraining: Rather than requiring labeled data (which is scarce in finance), these models use self-supervised learning on unlabeled time series. They might learn by forecasting missing pieces of a sequence or contrasting augmented views of the data (methods like SimCLR, TS2Vec, etc., as studied in the paper ([2310.03916] Toward a Foundation Model for Time Series Data) ([2310.03916] Toward a Foundation Model for Time Series Data)). This way, the model learns fundamental structures – trends, seasonality, anomalies – without human annotation.
Adaptability: Once pre-trained, the foundation model can be fine-tuned quickly on specific tasks or used directly in a zero-shot fashion. Fine-tuning is much faster than training from scratch because the model already knows “how to interpret” time series patterns. In fact, the research found that pre-training leads to a smoother and faster convergence during fine-tuning, meaning models reach optimal accuracy in less time ([2310.03916] Toward a Foundation Model for Time Series Data) ([2310.03916] Toward a Foundation Model for Time Series Data). For financial firms, this adaptability implies quicker development of models for new markets or assets.
Generalization: Because it isn’t niche to one domain, a foundation model carries cross-domain insights. Patterns learned from, say, industrial sensor data (like periodic maintenance cycles) might help it recognize analogous cyclical patterns in stock prices or economic indicators. This broad time series AI in investing context means the model is less likely to overfit to one market’s quirks and can handle regime changes or new instruments more robustly.
In summary, a time series foundation model serves as a powerful baseline brain that analysts can apply to many forecasting and classification problems, including those in finance, with minimal additional training. As the paper’s authors demonstrate, the era of training isolated models for each dataset is giving way to pre-trained generalists that one can specialize quickly (Moirai: A Time Series Foundation Model for Universal Forecasting) (A decoder-only foundation model for time-series forecasting).
Breaking the Single-Domain Barrier: Key Findings from the Research
The paper “Toward a Foundation Model for Time Series Data” ([2310.03916] Toward a Foundation Model for Time Series Data) by Yeh and collaborators (presented at CIKM 2023) tackled a crucial question: Can a single model pre-trained on many types of time series outperform models trained on one domain at a time? The researchers set up a rigorous study using the diverse UCR Time Series Archive as a source of multi-domain data ([2310.03916] Toward a Foundation Model for Time Series Data). Here are the key findings and takeaways from their work, distilled for financial AI practitioners:
Multi-Domain Pretraining Boosts Performance: Training a model on a large multi-domain dataset significantly improved its performance on specific tasks after fine-tuning ([2310.03916] Toward a Foundation Model for Time Series Data). In the experiments, a foundation model pre-trained across domains (e.g. including medical, environmental, and financial-like series) achieved higher accuracy on downstream single-domain tasks (like classifying a particular type of time series) compared to models that had not been pre-trained. This confirms that knowledge transfer happens: exposure to diverse patterns makes the model better at learning new ones. For financial forecasting, this suggests a model pre-trained on wide-ranging data can give better predictions on, say, stock trends than a model trained only on stock data.
Faster Convergence (Easier Training): An important practical win was that fine-tuning the foundation model was not only more accurate but also more efficient. The researchers observed much smoother convergence curves during fine-tuning ([2310.03916] Toward a Foundation Model for Time Series Data). In plain terms, when they started with the pre-trained model, the training process for a new task was quicker and more stable than starting from scratch. This is valuable in finance where model development time is money – a foundation model can cut down the iteration cycle for developing new trading models or portfolio strategies.
Transformer Architecture Shines: The study tested four neural network architectures (LSTM, GRU, ResNet, and Transformer) as the backbone for the foundation model ([2310.03916] Toward a Foundation Model for Time Series Data) ([2310.03916] Toward a Foundation Model for Time Series Data). The Transformer-based model consistently came out on top, especially when paired with the novel pretraining method the authors introduced. In fact, the best results were achieved by combining their proposed self-supervised learning approach with a Transformer encoder, outperforming all other architecture-method combos ([2310.03916] Toward a Foundation Model for Time Series Data). For quants, this underscores that modern transformer models (which excel at capturing long-range dependencies) are extremely effective for complex financial time series – capturing both short-term fluctuations and long-term trends.
New Pretraining Method (“TimeCLR”) Outperforms Alternatives: Alongside existing self-supervised techniques like SimCLR and TS2Vec, the authors developed a new method (dubbed TimeCLR). When used to pre-train the Transformer, TimeCLR led to state-of-the-art results on the evaluation tasks ([2310.03916] Toward a Foundation Model for Time Series Data). It outperformed or matched the next-best pretraining approach in roughly 93% of the downstream tasks they tested (Toward a Foundation Model for Time Series Data) – an impressively robust showing across 100+ different time series problems. This indicates the field is maturing, with specialized techniques that make pretraining even more effective for time-based data. While the technical details of TimeCLR are beyond our scope here, the takeaway is that advanced contrastive learning strategies can make foundation models even better at distilling useful features from unlabeled financial data.
Overall, the research provides proof of concept that foundation models for time series are not only feasible but highly beneficial. A model trained on myriad data can serve as a universal starting point and deliver strong performance on specialized tasks with minimal tuning ([2310.03916] Toward a Foundation Model for Time Series Data). For the finance domain, we can extrapolate that a single model could be pretrained on an amalgam of market data (stocks, bonds, commodities, etc. across decades) and alternative data for market prediction (economic indicators, satellite imaging of retail parking lots, social media sentiment, etc.), and then swiftly fine-tuned to predict, for example, sector-specific trends or detect credit card fraud. The heavy lifting (learning what a generic spike or seasonal pattern looks like) would already be done by the foundation model.
Implications for Financial Forecasting and Trend Prediction
Financial time series forecasting stands to be one of the biggest beneficiaries of these foundation models. Traditionally, forecasting models in finance – whether predicting stock prices, interest rates, or economic metrics – have been developed and optimized in silos. A quant team might build one model for equities, another for macroeconomic time series, and yet another for high-frequency trading signals. Each model would require substantial training data and tuning to handle the nuances of its domain. This siloed approach is resource-intensive and often means that insights learned in one domain (say, pattern of a volatility spike) don’t carry over to others.
Foundation models change this paradigm. By training on multi-domain time series data, they capture a broad spectrum of temporal dynamics that are common across datasets. For instance, the concept of seasonality or cyclical behavior is present in retail sales, electricity demand, and yes, even some financial indicators (think quarterly earnings cycles or seasonal market trends). A foundation model will have seen all forms of seasonality during pretraining and thus can recognize and model it quickly in a financial context. Similarly, the notion of an abrupt regime shift (an anomaly or shock) might be learned from earthquake sensor data or web traffic spikes and then help in modeling market crashes or surges.
For financial analysts, this means improved financial trend prediction in several ways:
Better Accuracy with Less Data: Foundation models can achieve high accuracy even when fine-tuned on relatively small datasets ([2310.03916] Toward a Foundation Model for Time Series Data). A common challenge in finance is limited labeled data (e.g., a new stock with little history, or rare economic events). A pre-trained model already has learned generic patterns of trends and variability, so it can make meaningful predictions from a short series. This few-shot capability was demonstrated in related work by Google: their TimesFM model, even at 200 million parameters (much smaller than giant language models), delivered near state-of-the-art forecasts on unseen datasets across different domains without any retraining (A decoder-only foundation model for time-series forecasting). In practice, this could allow a fund to deploy a workable forecasting model on a new asset class out-of-the-box, then gradually fine-tune it as more data arrives – significantly shortening the model development cycle.
Rapid Adaptation to Market Regimes: Markets are notorious for regime changes (bull to bear, low to high volatility regimes) that can trip up models trained in one regime. A foundation model’s broad training makes it more generalizable to shifting conditions. Fine-tuning can be done periodically with recent data to adapt the model, but because the model doesn’t unlearn the fundamentals, it can adapt faster and more robustly. The research paper’s finding of smoother convergence means updates to the model incorporate new patterns without losing stable performance ([2310.03916] Toward a Foundation Model for Time Series Data). For example, if sudden inflation creates new correlations in the data, a foundation model might catch on faster during fine-tuning than a naive model would.
Zero-Shot and Few-Shot Forecasting: We’re approaching a scenario where a well-trained time series foundation model could be applied to a completely new forecasting task with minimal or no additional training. This is akin to prompting ChatGPT with a task it wasn’t explicitly trained for. As an analogy, consider feeding the foundation model a time series of a novel alternative asset (say a new cryptocurrency) and asking for a forecast – if the model’s seen enough varied patterns, it might produce a reasonable prediction purely from its “experience”. Indeed, researchers have noted that large pre-trained models can offer “decent out-of-the-box forecasts on unseen time-series data with no additional training” (A decoder-only foundation model for time-series forecasting). In finance, this agility could be a game-changer: imagine being able to gauge a new market’s behavior instantly, then only lightly fine-tuning the model to polish the predictions.
Unified Modeling Across Assets: Instead of maintaining separate models for each asset or each frequency (daily vs intraday) – which is the current norm – firms could leverage one foundation model for all. This universal forecaster approach has been shown to handle diverse frequencies and even multi-variate inputs in research prototypes (Moirai: A Time Series Foundation Model for Universal Forecasting) (Moirai: A Time Series Foundation Model for Universal Forecasting). For a portfolio manager, this means one AI system could simultaneously forecast stock prices, interest rate moves, and commodity demand, understanding how they might interrelate. This holistic view is particularly valuable for multi-asset strategies and risk management, where understanding cross-market patterns is key.
In summary, foundation models bring the latest AI for financial forecasting into alignment with how quants think about markets – drawing parallels across domains, learning from history broadly, and adapting quickly. Early evidence indicates higher forecast accuracy and reliability can be achieved, which can translate directly to better decision-making and alpha generation for investors.
Enhancing Market Signal Detection with Broad Knowledge
Beyond point forecasts, one of the tantalizing prospects of time series foundation models is improved market signal detection. In finance, “signals” can mean anything from an arbitrage opportunity, to an emerging risk indicator, to an anomaly that warrants investigation. Detecting such signals often boils down to identifying patterns or outliers in complex data that spans multiple sources.
Because foundation models learn a rich representation of time series data, they can be used to surface subtle patterns that might be missed by traditional models. For example, an AI trained on millions of time series might recognize the early signs of a volatility regime change in a stock index because it has seen analogous patterns in other contexts (like how heart rate variability might precede a health event, or how network traffic surges precede a cyber-attack). The research by Yeh et al. was focused on classification tasks, and indeed a pre-trained model showed superior ability to classify time series patterns correctly ([2310.03916] Toward a Foundation Model for Time Series Data) – which directly translates to better signal detection in finance. A well-trained foundation model could more accurately classify whether a pattern in price data is an “uptrend,” a “mean-reversion signal,” or an anomaly, even with few examples to learn from.
Moreover, these models might enable earlier detection of signals. Because the foundation model has a memory of many temporal shapes, it can match a current snippet of data to patterns it has seen before. Think of an early warning system: if a certain combination of indicators preceded past market crashes (even in different countries or eras), a foundation model might flag a similar combination forming in today’s data. This kind of pattern recognition across disparate historical episodes is something humans struggle with (no one person can recall all instances), but a foundation AI can encode it in its weights.
In practice, financial firms could deploy foundation models to monitor streams of market and alternative data in real-time, using the model’s embeddings or outputs to rank the most unusual or significant changes. For instance, detecting an unusual convergence between social media sentiment and price momentum could signal an upcoming news-driven jump. Because the model has seen so many alternative data for market prediction scenarios during pretraining, it knows what “normal” correlations look like and can spot when relationships break the norm.
The benefit is a unified signal detector that doesn’t require bespoke programming for each new type of event. Whether it’s spotting fraudulent transaction patterns (anomaly in time series of payments) or identifying a macroeconomic leading indicator turning point, a foundation model can provide a common engine to interpret the data. This greatly simplifies the infrastructure – one model (or a small family of models) could replace dozens of specialized anomaly detection systems.
Multi-Modal Trend Analysis and Alternative Data Integration
Financial markets are influenced by more than just price and volume time series. News headlines, social media trends, economic reports, weather events – these all come in different forms of data (text, images, sensor readings) but have timestamps and can be aligned with market timelines. One of the exciting frontiers opened by foundation models is the ability to perform multi-modal trend analysis, fusing traditional market data with alternative data sources to improve predictions.
Foundation models, by design, are not limited to a single data modality. The concept of a “foundation” model extends to architectures that can ingest multiple modalities (for example, models like OpenAI’s GPT-4 can process text and images). For time series, this means we could have a model that simultaneously processes numeric time series and related text (like news feeds or Twitter signals) to generate a holistic view. In fact, researchers have begun exploring using large language models with numeric data, noting that incorporating textual context with time series can boost forecasting accuracy (Financial Fine-tuning a Large Time Series Model). In a finance setting, consider how a sudden spike in a stock’s sentiment score (from news or social media) often precedes a price move. A multi-modal foundation model could learn this association during pretraining: it might ingest historical price series along with sentiment time series or even raw news text, learning that certain words or themes often coincide with market shifts.
Alternative data for market prediction becomes far more powerful when a model can seamlessly integrate it with market data. A conventional approach might be to engineer features from alternative data (e.g., compute a sentiment index from text, or a satellite-based foot traffic metric) and then feed those into a separate predictive model. But a foundation model could take raw forms – say, the sequence of daily news article embeddings – alongside price, and figure out the relationships on its own. The broad training regime would expose it to many such relationships (perhaps learning how weather data affects agricultural commodity prices, or how mobility data affects retail stocks). As a result, when fine-tuned or applied to a specific use case, the model can leverage these cross-modal patterns.
For example, multi-modal foundation models could enable:
News-aware Market Forecasts: The model watches news headlines (treated as a text sequence) in parallel with price charts. If certain phrases like “earnings surprise” or “merger talks” appear in the text stream, the model’s internal state for the stock’s time series could adjust, having learned that such phrases often lead the stock to jump. This goes beyond sentiment – it’s contextual understanding. The outcome is a forecast that is not just based on past price movements but is also responsive to real-world events in real-time.
Geo-spatial and Weather Data Integration: For commodities or insurance-related investing, weather time series or satellite images (converted to data) can be crucial. A foundation model might ingest satellite imagery of, say, oil storage or crop fields over time (which is image data with time dimension) along with prices. It could learn that certain visual patterns (like a growing shadow on satellite pictures indicating rising stockpiles) correlate with price drops. Thus it becomes capable of predicting inventory levels or supply gluts before official reports. This multimodal insight can give traders an edge.
Cross-Asset and Cross-Market Trends: A single model could learn relationships between markets – e.g., how bond yields (time series) and currency rates (time series) and central bank communications (text) interact. When a new policy statement is released, the model, having been trained on decades of such data, recognizes the pattern and how usually bonds and currencies react, thereby forecasting the impact more accurately. This is particularly relevant for macro traders and global allocators who juggle diverse data streams.
It’s important to note that while the paper by Yeh et al. did not explicitly delve into multimodal data (they focused on multiple domains of time series, primarily all numeric sequences), the foundation model framework naturally extends to multimodality (Foundation Models for Time Series Analysis: A Tutorial and Survey). The modular design (a powerful core model with adaptable input layers) means one can plug in different data types. We are already seeing early research and products in finance that move in this direction – effectively, “all data is alternative data” from the model’s perspective, whether it’s market quotes or external signals.
For financial product teams, this means foundation models could serve as a single platform for trend analysis, where previously they needed separate text analysis systems, numerical models, etc. The developments in time series AI hint at a future where an analyst can feed a wealth of heterogeneous data into one system and get coherent analytics out, simplifying workflows and unlocking deeper insights.
Conclusion: A New Chapter for Time Series AI in Investing
The emergence of foundation models for time series marks a paradigm shift in how we approach forecasting and analytics in finance. Instead of building narrow models that excel only in isolated tasks, we now strive for generalist models – ones that understand time series broadly and can be adapted to virtually any financial prediction or detection problem.
The research paper “Toward a Foundation Model for Time Series Data” ([2310.03916] Toward a Foundation Model for Time Series Data) provides encouraging evidence that this approach yields tangible benefits: improved accuracy, faster training, and the ability to leverage knowledge across domains. For quants and institutional investors, the implications are profound. We can expect:
More powerful forecasting tools: Models that have essentially seen “everything” and can forecast new scenarios with uncanny skill.
Accelerated model development: A lot of heavy lifting is done upfront in pretraining, so deploying a model for a new market or client need becomes faster – keeping pace with the ever-changing financial landscape.
Richer insights: Through multi-modal trend analysis and universal feature learning, these AI systems will surface connections in data that humans might miss, from early-warning signals to cross-asset correlations.
Democratization of AI in finance: As these foundation models become available (indeed, some like TimesFM are open-sourced (A decoder-only foundation model for time-series forecasting)), even smaller firms or teams with limited data can leverage a pre-trained model and fine-tune it to their niche. This lowers the barrier to entry for advanced time series AI in investing applications.
Of course, this is just the beginning. As the authors note, there are exciting directions to explore – combining multiple self-supervised objectives, incorporating data compression ideas, and trying out new architectures ([2310.03916] Toward a Foundation Model for Time Series Data). In finance specifically, we will likely see specialized foundation models that incorporate financial inductive biases or knowledge of economic theory, further enhancing performance.
In conclusion, the advances described in Toward a Foundation Model for Time Series Data are changing the game for financial time series forecasting and analysis. They herald a future in which AI models trained on diverse alternative data for market prediction can detect market signals and predict trends with a breadth and depth of understanding that was previously unattainable. For those in the investing world, staying abreast of these developments is crucial – the next generation of quant models will not just be trained, but pre-trained. And as these foundation models continue to evolve, they promise to become an indispensable foundation for the financial industry’s decision-making, much like their NLP counterparts have become in their domains.
Sources:
Yeh et al., Toward a Foundation Model for Time Series Data, CIKM 2023. ([2310.03916] Toward a Foundation Model for Time Series Data) ([2310.03916] Toward a Foundation Model for Time Series Data) (Toward a Foundation Model for Time Series Data)
Salesforce AI Research, Moirai: A Time Series Foundation Model for Universal Forecasting (blog) (Moirai: A Time Series Foundation Model for Universal Forecasting) (Moirai: A Time Series Foundation Model for Universal Forecasting)
Google AI Blog, A decoder-only foundation model for time-series forecasting (A decoder-only foundation model for time-series forecasting) (A decoder-only foundation model for time-series forecasting)
Fu et al., Financial Fine-tuning a Large Time Series Model, arXiv 2023. (Financial Fine-tuning a Large Time Series Model)
Zerveas et al., A Tutorial on Time Series Foundation Models, arXiv 2024.