Summary Large Language Models for Time Series Forecasting arxiv.org
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One Line
Large language models can accurately predict time series data by treating it as text, surpassing specialized methods and effectively incorporating additional textual information.
Slides
Slide Presentation (11 slides)
Key Points
- Large language models (LLMs) like GPT-3 and LLaMA-2 can be used for time series forecasting by treating it as a next-token prediction task in text.
- LLMs can naturally represent multimodal distributions and handle missing data without imputation, making them effective for time series forecasting.
- The authors propose procedures to convert discrete distributions over tokens into flexible densities over continuous values for tokenizing time series data.
- LLMTIME, a method that applies pretrained LLMs for continuous time series prediction, can achieve high performance without fine-tuning on downstream data.
- LLMs have preferences for simple or repetitive sequences, aligning with the structure of time series, and can express multimodal distributions.
Summaries
29 word summary
Large language models (LLMs) like GPT-3 and LLaMA-2 can forecast time series by treating them as text. They handle missing data and incorporate textual side information, outperforming purpose-built methods.
101 word summary
Large language models (LLMs) such as GPT-3 and LLaMA-2 can forecast time series by treating them as a string of digits and predicting the next token in text. The authors propose methods to tokenize time series data effectively, using flexible densities to represent discrete distributions. LLMs can handle missing data, incorporate textual side information, and bridge the gap between traditional methods and deep learning. LLMTIME is a method that applies pretrained LLMs for continuous time series prediction, outperforming purpose-built methods without fine-tuning. LLMs show promise for time series forecasting by leveraging their natural language processing capabilities and pretraining on large-scale tasks.
140 word summary
Large language models (LLMs) like GPT-3 and LLaMA-2 can be used for time series forecasting by treating time series as a string of numerical digits and predicting the next token in text. To tokenize time series data effectively, the authors propose procedures that convert discrete distributions over tokens into flexible densities over continuous values. LLMs can handle missing data without imputation and can incorporate textual side information to explain predictions. LLMs bridge the gap between traditional time series methods and deep learning by combining their biases and generative abilities. The authors introduce LLMTIME, a method that applies pretrained LLMs for continuous time series prediction. LLMTIME can match or outperform purpose-built methods without fine-tuning on downstream data. Overall, LLMs offer a promising approach for time series forecasting by leveraging their natural language processing capabilities and pretraining on large-scale language modeling tasks.
565 word summary
Large language models (LLMs) like GPT-3 and LLaMA-2 can be used for time series forecasting by treating time series as a string of numerical digits and predicting the next token in text. This approach allows LLMs to extrapolate time series with similar or better performance than purpose-built time series models. LLMs can represent multimodal distributions and have biases that align with the salient features in many time series.
To tokenize time series data effectively, the authors propose procedures that convert discrete distributions over tokens into flexible densities over continuous values. LLMs can handle missing data without imputation and can incorporate textual side information to explain predictions. Increasing model size generally improves performance on time series, but GPT-4 may perform worse than GPT-3 due to tokenization issues and poor uncertainty calibration.
Time series forecasting presents unique challenges compared to other sequence modeling problems. Time series data often consists of sequences from different sources with missing values. Accurate point predictions are difficult due to limited information, making uncertainty estimation crucial. Large-scale pretraining is common in vision and text modeling but not typically used for time series modeling due to the lack of consensus unsupervised objectives and cohesive pretraining datasets.
LLMs bridge the gap between traditional time series methods and deep learning by combining their biases and generative abilities. The authors introduce LLMTIME, a method that applies pretrained LLMs for continuous time series prediction. LLMTIME can match or outperform purpose-built methods without fine-tuning on downstream data.
LLMTIME eliminates the need for specialized knowledge and computational resources required for fine-tuning. It is well-suited for scenarios with limited data availability and reduces the time and effort typically required for crafting dedicated time series models.
LLMs have preferences for simple or repetitive sequences, which align with the structure of time series. They can handle missing data and represent multimodal distributions. The quality of point predictions and uncertainty representation improves with the scale and reasoning performance of the underlying model. However, GPT-4 may have worse uncertainty calibration due to alignment interventions.
LLMTIME achieves strong performance on standard time series benchmarks and performs well on unseen datasets. It is more sample-efficient and can handle missing values without imputation. LLMs can also answer questions and provide explanations for their predictions. However, forecasting performance may be degraded in chat models designed for chatbot applications.
Overall, LLMs offer a promising approach for time series forecasting by leveraging their natural language processing capabilities and pretraining on large-scale language modeling tasks.
The study discusses the use of LLMs for time series forecasting, providing details about the method, hyperparameters used, scaling, and validation tuning processes. The authors address concerns about memorization in LLM evaluations and perform experiments using time series recorded after LLM's training data cutoff date to show that its performance is not solely due to memorization.
The authors provide details about benchmark datasets, extended results, and visualization of predictions on different datasets. They conduct simplicity bias experiments using synthetic datasets and evaluate the use of GPT-4 for time series prediction, discussing limitations of the tokenizer.
The study evaluates the model's ability to reason about time series through text in a zero-shot fashion and presents sample outputs and analysis.
In conclusion, the study explores the use of large language models for time series forecasting. The authors discuss their methods, address concerns about memorization, present experimental results, and evaluate the model's ability to reason about time series through text.
620 word summary
Large language models (LLMs) like GPT-3 and LLaMA-2 can be utilized for time series forecasting by treating time series as a string of numerical digits and predicting the next token in text. This approach allows LLMs to extrapolate time series with similar or better performance than purpose-built time series models. LLMs are able to represent multimodal distributions and have biases that align with the salient features in many time series.
To effectively tokenize time series data, the authors propose procedures that convert discrete distributions over tokens into flexible densities over continuous values. They also demonstrate that LLMs can handle missing data without imputation and can incorporate textual side information to explain predictions. Increasing model size generally improves performance on time series, but GPT-4 may perform worse than GPT-3 due to tokenization issues and poor uncertainty calibration.
Time series forecasting presents unique challenges compared to other sequence modeling problems. Time series data often consists of sequences from different sources with missing values. Accurate point predictions are difficult due to the limited information available, making uncertainty estimation crucial. While large-scale pretraining is common in vision and text modeling, it is not typically used for time series modeling due to the lack of consensus unsupervised objectives and cohesive pretraining datasets.
LLMs bridge the gap between traditional time series methods and deep learning by combining their biases and generative abilities. The authors introduce LLMTIME, a method that applies pretrained LLMs for continuous time series prediction. LLMTIME can match or outperform purpose-built methods across various problems without fine-tuning on downstream data.
The zero-shot nature of LLMTIME eliminates the need for specialized knowledge and computational resources required for fine-tuning. It is well-suited for scenarios with limited data availability and reduces the time and effort typically required for crafting dedicated time series models.
LLMs have preferences for simple or repetitive sequences, which align with the structure of time series. They can handle missing data and represent multimodal distributions. The quality of point predictions and uncertainty representation improves with the scale and reasoning performance of the underlying model. However, GPT-4 may have worse uncertainty calibration due to alignment interventions.
LLMTIME achieves strong performance on standard time series benchmarks and performs well on unseen datasets. It is more sample-efficient and can handle missing values without imputation. LLMs can also answer questions and provide explanations for their predictions. However, forecasting performance may be degraded in chat models designed for chatbot applications.
Overall, LLMs offer a promising approach for time series forecasting by leveraging their natural language processing capabilities and pretraining on large-scale language modeling tasks.
The study discusses the use of LLMs for time series forecasting and provides details about the method and hyperparameters used. The authors explain the scaling and validation tuning processes and address concerns about memorization in LLM evaluations.
To address the memorization concern, the authors perform experiments using time series recorded after LLM's training data cutoff date. They find that LLM continues to outperform baselines, suggesting that its performance is not solely due to memorization.
The authors provide details about benchmark datasets and extended results used in their experiments, including visualization of predictions on different datasets.
The authors conduct simplicity bias experiments using synthetic datasets and rank solutions based on complexity.
The authors investigate the use of GPT-4 for time series prediction and discuss limitations of the tokenizer.
They also evaluate the ability of the language model to reason about time series through text in a zero-shot fashion and present sample outputs and analysis.
In conclusion, the study explores the use of large language models for time series forecasting. The authors discuss their methods, address concerns about memorization, present experimental results on benchmark datasets, and evaluate the model's ability to reason about time series through text.
1110 word summary
Large language models (LLMs) such as GPT-3 and LLaMA-2 can be used for time series forecasting by encoding time series as a string of numerical digits and treating forecasting as next-token prediction in text. This approach allows LLMs to zero-shot extrapolate time series at a level comparable to or exceeding the performance of purpose-built time series models. The success of LLMs for time series stems from their ability to naturally represent multimodal distributions and their biases for simplicity and repetition, which align with the salient features in many time series.
To effectively tokenize time series data, the authors propose procedures that convert discrete distributions over tokens into highly flexible densities over continuous values. They also demonstrate that LLMs can naturally handle missing data without imputation through non-numerical text and can accommodate textual side information and answer questions to explain predictions. Increasing model size generally improves performance on time series, but the authors show that GPT-4 can perform worse than GPT-3 due to how it tokenizes numbers and poor uncertainty calibration resulting from alignment interventions.
Time series forecasting has unique challenges compared to other sequence modeling problems. Unlike video or audio, time series data often comprises sequences from radically different sources with missing values. Common applications of time series forecasting require extrapolating from observations that contain a tiny fraction of the possible information, making accurate point predictions nearly impossible and uncertainty estimation especially important. While large-scale pretraining has become a key element of training large neural networks in vision and text, pretraining is not typically used for time series modeling due to the lack of consensus unsupervised objectives and large, cohesive pretraining datasets.
LLMs naturally bridge the gap between the biases of traditional time series methods and the generative abilities of deep learning. The authors introduce LLMTIME, a method that applies pretrained LLMs for continuous time series prediction problems. They propose techniques to effectively encode time series as a string of numerical digits and adapt the discrete distributions of LLMs to continuous densities capable of modeling multimodal distributions. LLMTIME can exceed or match purpose-built time series methods over a range of different problems in a zero-shot fashion, meaning it can be used without any fine-tuning on the downstream data used by other models.
The zero-shot nature of LLMTIME provides several advantages. It eliminates the need for specialized knowledge of fine-tuning procedures and the substantial computational resources required for these procedures. It is naturally suited to scenarios with limited data availability and circumvents the extensive time, effort, and domain-specific expertise typically required for crafting dedicated time series models.
LLMs have preferences for simple or repetitive sequences, which align with the salient structure of time series such as seasonality. They can naturally accommodate missing data and express multimodal distributions. In addition to forecasting performance, LLMTIME also improves with scale and the quality of point predictions improves with the quality of the uncertainty representation. However, GPT-4 has worse uncertainty calibration than GPT-3, likely due to alignment interventions such as reinforcement learning by human feedback.
LLMTIME achieves strong performance on standard time series benchmarks and performs well on datasets that could not have been present in the base model's training data. It is more sample-efficient than competing methods and can handle missing values without imputation. LLMs can also be used to answer questions about time series data and provide explanations for their predictions. The performance of LLMs in time series forecasting improves with the reasoning performance of the underlying model, but chat models designed for chatbot applications may have degraded forecasting performance.
Overall, LLMs offer a promising approach for time series forecasting by leveraging their natural language processing capabilities and pretraining on large-scale language modeling tasks.
The study focuses on the use of large language models (LLMs) for time series forecasting. The authors discuss the method and hyperparameters used in their experiments. They explain that for baseline methods, they apply the MinMaxScaler from sklearn to scale the input. However, for GPT-3, they use an affine transformation to handle inputs spanning multiple orders of magnitudes. They also mention a basic scaler that only applies scaling and not shifting. They describe the validation tuning process, where they construct a validation time series from the last observations in the training series. They use the likelihood of generating the validation conditioned on the remaining training series to select the hyperparameters.
The authors address concerns about memorization in GPT-3 evaluations. They mention that LLMs are known to memorize large amounts of their training data verbatim, including common benchmark text datasets and copyrighted material. However, they argue that it's unlikely that GPT-3's good performance is solely due to memorization for two reasons. First, their idiosyncratic formatting is unlikely to be present in the training dataset. Second, the time series datasets are unlikely to appear in GPT-3's training data frequently enough to lead to memorization.
To further address the memorization concern, the authors perform a direct experiment using time series recorded after GPT-3's training data cutoff date. They evaluate GPT-3's performance on three time series datasets and compare it against popular time series models. They find that GPT-3 continues to be competitive with or outperform the baselines on all of the tasks, which reinforces their belief that GPT-3's performance is not due to memorization of the test data.
The authors provide details about the benchmark datasets and extended results used in their experiments. They describe the Darts datasets, Monash datasets, and Informer datasets. For each dataset, they explain the baseline methods used and the hyperparameters tuned. They also include visualizations of GPT-3's predictions on the Monash and Informer datasets.
The authors conduct simplicity bias experiments using synthetic datasets. They generate data from various functions and fit symbolic expressions to the data. They rank the solutions by complexity and evaluate the likelihoods using GPT-3.
The authors investigate the use of GPT-4 for time series prediction. They mention the limitations of the tokenizer and the need for additional commands to coax GPT-4 to produce numerical predictions that can be decoded.
The authors evaluate the ability of the language model to reason about time series through text in a zero-shot fashion. They devise a simple experiment where they generate a synthetic time series from one of several candidate functions. They provide the generation code and the numerical values to GPT-4, which must infer which function generated the values. They present sample outputs for different time series and discuss the model's analysis and reasoning.
In conclusion, the study explores the use of large language models for time series forecasting. The authors discuss their methods, address concerns about memorization, present experimental results on benchmark datasets, and evaluate the model's ability to reason about time series through text.