Revolutionize your business strategy with AI-powered innovation consulting. Unlock your company's full potential and stay ahead of the competition. (Get started for free)

The Forecasting Paradox Why Time Series Prediction Lags Behind LLM Evolution Despite Shared Foundations

The Forecasting Paradox Why Time Series Prediction Lags Behind LLM Evolution Despite Shared Foundations - The Lagging State of Time Series Models Behind GPTs Despite Similar Neural Network Origins

While both time series models and GPTs are rooted in neural networks, there’s a noticeable disparity in their progress. Time series forecasting struggles with inherent delays when using past data to predict the future. This is particularly true for many economic and financial datasets which behave similarly to random walks, making them tough to predict. Unlike fields like NLP and computer vision, time series typically requires specific models for different kinds of data. This fragmentation slows down progress. While the use of neural networks in time series is growing, with many studies available, the need for better ways to manage time delays, including selecting the right time-lag, is a major sticking point. The ability of models to push past data into future predictions is lacking and needs urgent attention.

Despite the shared origins in neural networks, it is noteworthy how time series models still trail behind the advancements seen in Large Language Models (LLMs). This divergence isn't simply about raw computational power; it's about fundamental differences in the data and approaches taken. Time series data, unlike text, often exhibits non-stationarity, a moving target that limits many prediction methods, while the adaptable models powering language models seem less bothered by this. Even though both fields use neural networks, the complexities of how time is handled in temporal data makes it hard for models to handle big data in the same way language models can. The need for easy to understand forecasts in time series analysis has historically favored simpler models like ARIMA, instead of harnessing the power of deep learning in many language understanding feats. Plus, while language models get trained on vast amounts of text, time series data needs to be specific to each application, making a broadly applicable model harder to find. Time series data also has seasonality and trends woven within it making it different from the usually static data sets used in language model training slowing progress of techniques in time. Though potentially powerful, models for time series often struggle with the “vanishing gradient” problem, which pushes researchers to favor architectures used in language work like transformers, resulting in slow progress in time series methods. Moreover, the metrics used to judge performance, like MAE and MSE in time series, are very different from how language models are evaluated, and this adds a further barrier to shared progress. Another point to consider, the limited focus and funding for time series prediction seems to be playing a part in slower research. It seems we need more investment to close the gap between time series models and the progress seen in language model applications. It does not help that the data needs of training effective models for time series is not trivial due to sparse historical data that easily leads to overfitting whereas vast, unlabeled, text data is readily available for language models. Finally, an almost stubborn adherence to long standing, tried and true traditional time series forecasting approaches appears to be a large contributing factor to the slow pace when compared to the rapid development of LLMs.

The Forecasting Paradox Why Time Series Prediction Lags Behind LLM Evolution Despite Shared Foundations - Why External Factors Still Make Stock Market Predictions Hard for Latest Models

External factors render stock market predictions particularly challenging for even the most advanced models. The nonstationarity of financial data means that its properties can shift dramatically over time, complicating model reliability. Additionally, external influences such as economic fluctuations, regulatory transformations, and technological advancements add layers of complexity that traditional and even some machine learning forecasting approaches struggle to integrate. Models often fall short due to inadequate data quality or an inability to adapt to sudden market shifts, resulting in misguided predictions driven by investor psychology. This persistent reliance on historical data in forecasting leads to inaccuracies, emphasizing the need for models that can evolve and keep pace with the volatile nature of financial markets.

Stock market predictions remain notoriously difficult, even for today’s advanced models, due to the inherent complexity and unpredictability of financial systems. The behavior of these markets is often chaotic, swayed by a huge array of external factors such as political events, economic policies, and global crises, creating non-linear behavior that algorithms frequently fail to fully grasp. Market sentiment, prone to rapid shifts based on news or public perception, exerts a strong influence over price movements. This makes it challenging as approaches to analyze the complex sentiment often return conflicting data, further undermining the accuracy of forecasting methods. The interconnectedness of the global economy further adds complexity as events in one nation can have unexpected repercussions in others making localized data sometimes inadequate to capture the broader, emergent, trends. The rise of algorithmic trading introduces significant volatility, as these systems react with near instantaneous speed, creating fast feedback loops that overwhelm traditional models. Sudden regulatory changes are capable of altering market dynamics overnight, creating a world that previous models have not anticipated, rendering them less effective. Furthermore, inconsistencies in data quality across different markets and sectors add another barrier, as unreliable data can misguide models trained on incomplete information. Human psychology also plays a part, as cognitive biases often cause irrational market movements that challenge the very foundations of predictive models. The characteristics of financial time series are often changing due to evolving market conditions, resulting in a moving target for models. Such structural changes can undermine the value of historical data for model learning. Seasonality and anomalies add another layer of complexity. Though seasonality can be useful in some instances, it can significantly vary, making it difficult to generalize findings. Anomalies, such as flash crashes or rallies, can break established patterns and decrease model accuracy. Lastly, market participants engage in complex collaborative trading patterns, where decisions are influenced by peer behavior and not just independent analysis, further complicating predictability using typical methods.

The Forecasting Paradox Why Time Series Prediction Lags Behind LLM Evolution Despite Shared Foundations - The Training Data Problem That Makes LLMs Better at Text Than Numbers

Large Language Models (LLMs) excel at generating text due to massive training on extensive textual datasets. This focus however contrasts sharply with their performance on structured numerical information, where they fall short of their natural language processing prowess. Time series forecasting requires specialized data handling and models tailored for unique challenges like time lags or unpredictable patterns which LLMs architectures aren't inherently designed to handle. The diverse applications and data specific needs of time series data requires very different approaches compared to the more unified structure of LLM task performance. Adapting LLMs for numerical data, like time series, requires addressing specific preprocessing and fine-tuning problems that still limit their usefulness despite advances in shared foundational approaches.

It's interesting how much better large language models (LLMs) are at text compared to numerical tasks, particularly time series forecasting. This isn't because neural nets, in theory, cannot do both, but it seems to be how they are used and how the data appears. For one, there's the sheer amount of data. LLMs are practically swimming in textual data; it’s massive. Time series forecasting, on the other hand, often deals with far more limited historical data, which can result in models that are really good at what they trained on, but not so much if data drifts slightly. The type of data itself is another big factor, as text has a certain kind of inherent structure that seems to allow these models to discover patterns much more easily. Time series data often has a complex mix of seasonality, trends, and sudden shifts that can really throw models off. Also, how we measure success is different. LLM progress uses benchmarks that focus on coherence and accuracy, while time series forecasting has metrics like MAE or MSE, focusing on the numeric magnitude of errors. These differences further the divide. Overfitting can become a big problem. Time series models struggle when using very specific data since overfitting is a major hurdle, something less common in the broader domain of text processing due to the large, unlabelled data sets available. Transformers, while quite the phenomenon in NLP, haven't had the same impact on time series. Old, traditional, approaches that aren't so adept at finding temporal patterns still seem to dominate the field, resulting in models that are still behind. The idea of time dependence is also interesting, as it assumes data behavior is similar over the course of time (stationarity), and that's hardly ever the case with most real-world datasets, and thus makes predicting into the future more of a guess. It might be that language data, with its high entropy, allows models to pick up on a wide range of subtleties that numerical data often lacks. Since time series predictions often need to rely on past data, they can seem to always have a time lag to their usefulness. LLMs have no such limit and can incorporate new data faster. A big difference seems to also be the ability of models to make connections and provide insights that include human behavior, that language models seem to be more adapt at analyzing sentiment from media, while times series models usually just ignore such factors, leading to, at times, inferior predictions. Finally, there's the imbalance in investment. There's so much focus and money on NLP and LLMs while comparatively little attention goes to time series forecasting; this probably isn't helping to close the performance gap.

The Forecasting Paradox Why Time Series Prediction Lags Behind LLM Evolution Despite Shared Foundations - Recursive Error Accumulation Remains a Core Challenge in Time Series Work

person using macbook air on brown wooden table,

Recursive error buildup continues to be a core difficulty in time series work. As forecasts reach further into the future, the accuracy of predictions tends to worsen. This occurs because, while the concept of repeatedly using prior predictions to predict future values seems straightforward, it inherently creates a scenario where small inaccuracies grow and amplify over time. Even advanced techniques such as LSTM networks, often used for time-series analysis, fall victim to this effect because of their reliance on previously generated predictions. This reinforces the idea that there is a need for novel strategies tailored to account for the multiple interconnected forecasts, rather than simple iterative approaches that do not address compounded errors. While current research explores methods to directly address this issue, this underscores that effectively managing and modeling these cumulative errors is still essential to improving time series prediction performance. Techniques like introducing noise into past steps seek to lessen this, highlighting how future developments are essential to tackling the challenges inherent in temporal predictions.

Recursive error build-up remains a persistent headache in time series work, as even small errors in earlier predictions tend to magnify over time. This creates a cascading effect, making longer forecasts increasingly questionable. Time series data is unlike static datasets, in that errors get passed along and change their nature, which makes training these models difficult and can easily result in models understating actual error. The reliance on predicted values as inputs for further prediction generates feedback loops that can cycle errors back into the models creating further distortions to expected values. The commonly used metrics like MAE and RMSE don't tell the whole story, since they don’t account for the time dependence within the data, and can thus paint an inaccurate picture of how the model is really doing. Although methods like state space models and Kalman filters, which have specific ways to deal with error accumulation exist, these are not always used because these methods are quite hard to understand and use compared to old methods like ARIMA. Picking the right time lag for prediction is even more of a problem because if the time-lag is off, then existing errors are simply made worse as time goes on. Interestingly, even approaches like ensemble models, which typically help reduce error by combining a range of predictions, can sometimes struggle when temporal effects get combined with error buildup. Changes in the underlying data, or non-stationarity, makes recursive error worse, because models that do not adapt to changing trends in the data tend to get worse very rapidly when this effect appears. Researchers try hybrid approaches that combine classic and modern techniques to combat the problem of error accumulation, but, it's still not clear how to achieve the right balance between complexity and understanding. On a more positive note, some data suggests that model re-training or updates could help to control error accumulation, but those strategies can also be expensive in terms of computation, and require a solid plan to deal with all the added data.

The Forecasting Paradox Why Time Series Prediction Lags Behind LLM Evolution Despite Shared Foundations - What the 2023 M6 Financial Forecasting Competition Revealed About Model Limitations

The 2023 M6 Financial Forecasting Competition revealed key shortcomings in current forecasting models, notably stressing the need to rethink how we approach financial time series data. A shift from previous events, the M6 competition emphasized cross-sectional forecasting, indicating how traditional time series methods often fail to capture the intricacies of financial markets. Despite high hopes, the competition highlighted the ongoing problem of overconfidence in models, in addition to the influence of human biases, both substantial elements that may distort forecasts. Moreover, the short one-week forecasting period revealed disparities in how well models performed, pointing to complicated interactions in finance where old approaches have trouble adapting. In the end, the competition reconfirmed that despite progress, major gaps remain in understanding and handling the complex difficulties of financial forecasting.

The 2023 M6 Financial Forecasting Competition brought to light some key weaknesses in current modeling practices. The sheer variety of methods used—over a hundred different approaches—highlights how fragmented time series modeling still is, and that one-size-fits-all strategies are unrealistic, even when all the models start from similar neural net architectures. Performance often got worse the further out the predictions went, showcasing the recursive nature of error buildup in financial time series and the challenges this poses for long-term forecasting. It was also clear that data quality plays a crucial role; flawed data often derailed models, no matter how sophisticated, pointing out a limitation that's more fundamental than the algorithms themselves. Many still favored old approaches like ARIMA, even when better models are theoretically available, which may be keeping progress from moving forward. A big oversight was that many failed to take into account sudden changes or breakpoints in the data which was probably caused by using models which are incapable of understanding data that is not uniform. Small tweaks in how models are set up also produced wildly varying results highlighting how sensitive these methods can be to parameter selection. Even attempts to use combined or ensemble models had problems with diminishing results and did not improve predictions as would be expected. It was also a bit disheartening to see a lack of truly inventive techniques; most forecasts were just modified existing methods, suggesting that more original ideas could benefit the field. The problem of relying too much on historical data was apparent too, with few attempts to use current info, showing that forecasting needs to be more adaptive. Finally, many models fell prey to overfitting due to limited data, indicating that robustness is still a big issue, and it is not enough to simply build bigger and more complex models.

The Forecasting Paradox Why Time Series Prediction Lags Behind LLM Evolution Despite Shared Foundations - The Computing Power Gap Between Text Generation and Sequence Prediction Tasks

The computing power gap between text generation and sequence prediction tasks reflects the differences in model design and practical use. Text generation benefits from general-purpose models like LLMs, which can process diverse language aspects under one umbrella. Time series forecasting, conversely, often demands bespoke models tailored for specific datasets, making universal approaches more difficult. Also, LLMs often use sub-word tokens to boost their predictive abilities, while sequence prediction models struggle with the inherent complexities of time-based data. This fundamental disparity points to the hurdles in advancing time series techniques, especially given that it is lagging in both research and financial resources relative to text generation.

It’s striking how unevenly computing resources are distributed between text generation and time series tasks. LLMs often require weeks of training on massive clusters of GPUs, while solid time series models sometimes work perfectly fine after a few hours of calculations on regular hardware. It’s quite old-school, but many still turn to linear methods, like ARIMA for financial work even when more recent methods may be more suited to the task, despite massive computational progress. The lack of solid datasets often makes time series models struggle, whereas LLMs have vast text collections to learn from. This means time series often suffers from overfitting and biased analysis. The way that error propagates in time series models, adding up over time, can really bring down the accuracy and we have seen how this can create major problems for models that rely on using prior outputs to generate new predictions. Time series data changes all the time, which is not usually a problem for language tasks. Financial models, particularly, are subject to sudden shifts and the data today is not necessarily the same as it was yesterday. The metrics used to gauge success of time series models, like Mean Absolute Error (MAE), do not easily map to progress in language modeling, since coherence is favored over numerical precision. This makes it difficult to adopt better methods from both sides of research, and highlights the siloed nature of how research is often undertaken. Trying to understand and use insights on human behavior in financial work is another significant barrier, where sentiment and public perception is hard to model accurately for time series methods but is an easy task for many LLMs. Even though deep learning methods are used in time series models, their real world use is less common when compared to older linear methods. There seems to be a preference for those older methods due to them being more readily understood and applied in the typical forecasting work environment. It is disheartening that there seems to be less funding and resources devoted to time series research when compared to that seen in language modeling which likely further adds to why progress is so slow on the time series side. Perhaps most telling, events like the M6 Financial Forecasting Competition show how many models all struggle with the same issues. This really shows that model diversity itself is not always helpful. In fact, the shared issues across such diverse approaches seem to highlight fundamental problems in how we treat forecasting problems.