chronos-t5-tiny
Maintainer: amazon
75
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
chronos-t5-tiny is a family of pretrained time series forecasting models developed by Amazon based on the language model architecture of T5. These models transform a time series into a sequence of tokens using scaling and quantization, and then train a language model on these tokens using a cross-entropy loss. During inference, the model can autoregressively sample future trajectories to generate probabilistic forecasts. The chronos-t5-tiny model in particular has 8M parameters and is based on the t5-efficient-tiny architecture. This smaller model size allows for fast inference on a single GPU or even a laptop, while still achieving strong forecasting performance.
Compared to similar time series models like granite-timeseries-ttm-v1 from IBM and chronos-hermes-13b, the chronos-t5-tiny model has a more compact architecture focused specifically on time series forecasting. It also benefits from being part of the broader Chronos family of models, which have been trained on a large corpus of time series data.
Model inputs and outputs
Inputs
- Time series data: The model takes in a time series, which is transformed into a sequence of tokens through scaling and quantization.
Outputs
- Probabilistic forecasts: The model outputs probabilistic forecasts, where it autoregressively samples multiple future trajectories given the historical context.
Capabilities
The chronos-t5-tiny model is capable of producing accurate probabilistic forecasts for a variety of time series datasets, including those related to electricity demand, weather, and solar/wind power generation. It achieves state-of-the-art zero-shot forecasting performance, and can be further fine-tuned on a small amount of target data to improve accuracy. The compact size and fast inference speed of the model make it well-suited for real-world applications where resource constraints are a concern.
What can I use it for?
The chronos-t5-tiny model can be used for a wide range of time series forecasting applications, such as:
- Forecasting energy consumption or generation for smart grid and renewable energy applications
- Predicting demand for products or services to improve inventory management and supply chain optimization
- Forecasting financial time series like stock prices or cryptocurrency values
- Predicting weather patterns and conditions for weather-sensitive industries
The model's ability to provide probabilistic forecasts can also be useful for risk assessment and decision-making in these types of applications.
Things to try
One interesting aspect of the chronos-t5-tiny model is its use of a language model architecture for time series forecasting. This allows the model to leverage the powerful capabilities of transformers, such as capturing long-range dependencies and contextual information, which can be valuable for accurate forecasting. Researchers and practitioners may want to explore how this architecture compares to more traditional time series models, and investigate ways to further improve the model's performance through novel training techniques or architectural modifications.
Additionally, the compact size of the chronos-t5-tiny model opens up opportunities for deploying the model in resource-constrained environments, such as edge devices or mobile applications. Exploring efficient deployment strategies and benchmarking the model's performance in these real-world scenarios could lead to impactful applications of this technology.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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chronos-t5-large
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The chronos-t5-large model is a time series forecasting model from Amazon that is based on the T5 architecture. Like other Chronos models, it transforms time series data into sequences of tokens using scaling and quantization, and then trains a language model on these tokens to learn patterns and generate future forecasts. The chronos-t5-large model has 710M parameters, making it the largest in the Chronos family, which also includes smaller variants like chronos-t5-tiny, chronos-t5-mini, and chronos-t5-base. Chronos models are similar to other text-to-text transformer models like CodeT5-large and the original T5-large in their use of a unified text-to-text format and encoder-decoder architecture. However, Chronos is specifically designed and trained for time series forecasting tasks, while CodeT5 and T5 are more general-purpose language models. Model inputs and outputs Inputs Time series data**: The Chronos-T5 models accept sequences of numerical time series values as input, which are then transformed into token sequences for modeling. Outputs Probabilistic forecasts**: The models generate future trajectories of the time series by autoregressively sampling tokens from the trained language model. This results in a predictive distribution over future values rather than a single point forecast. Capabilities The chronos-t5-large model and other Chronos variants have demonstrated strong performance on a variety of time series forecasting tasks, including datasets covering domains like finance, energy, and weather. By leveraging the large-scale T5 architecture, the models are able to capture complex patterns in the training data and generalize well to new time series. Additionally, the probabilistic nature of the outputs allows the models to capture uncertainty, which can be valuable in real-world forecasting applications. What can I use it for? The chronos-t5-large model and other Chronos variants can be used for a wide range of time series forecasting use cases, such as: Financial forecasting**: Predicting stock prices, exchange rates, or other financial time series Energy demand forecasting**: Forecasting electricity or fuel consumption for grid operators or energy companies Demand planning**: Forecasting product demand to optimize inventory and supply chain management Weather and climate forecasting**: Predicting weather patterns, temperature, precipitation, and other climate-related variables To use the Chronos models, you can follow the example provided in the companion repository, which demonstrates how to load the model, preprocess your data, and generate forecasts. Things to try One key capability of the Chronos models is their ability to handle a wide range of time series data, from financial metrics to weather measurements. Try experimenting with different types of time series data to see how the model performs. You can also explore the impact of different preprocessing steps, such as scaling, quantization, and time series transformation, on the model's forecasting accuracy. Another interesting aspect of the Chronos models is their probabilistic nature, which allows them to capture uncertainty in their forecasts. Try analyzing the predicted probability distributions and how they change based on the input data or model configuration. This information can be valuable for decision-making in real-world applications.
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The chronos-hermes-13b model is a merge of the chronos-13b and Nous-Hermes-13b models, created by the maintainer Austism. This combined model has the evocative, descriptive nature of the Chronos model along with the improved coherency and ability to follow instructions from the Nous-Hermes model. The result is a language model that excels at producing vivid, narrative-driven outputs while maintaining a strong grasp of the task at hand. Model inputs and outputs The chronos-hermes-13b model is a text-to-text AI that takes natural language prompts as input and generates corresponding text outputs. The model was trained on a large corpus of data, including a mix of the datasets used to train the original Chronos and Nous-Hermes models. Inputs Natural language prompts of varying length and complexity Outputs Coherent, narrative-driven text outputs that can range from short responses to lengthy, descriptive passages Capabilities The chronos-hermes-13b model excels at tasks that require both imagination and structure, such as creative writing, story generation, and task-oriented dialogue. It can produce evocative, immersive outputs while maintaining a strong grasp of the user's instructions and the overall narrative flow. What can I use it for? The chronos-hermes-13b model could be useful for a variety of applications, such as: Creative writing assistance**: Help generate engaging story ideas, plot developments, and character descriptions to aid human writers. Conversational AI**: Deploy the model as a chatbot or virtual assistant that can engage in fluent, narrative-driven dialogue. Content creation**: Use the model to generate compelling text for marketing materials, blog posts, or other online content. Things to try One interesting aspect of the chronos-hermes-13b model is its ability to blend descriptive, imaginative writing with a clear understanding of the user's instructions. You could try giving the model prompts that ask it to generate a detailed story or narrative while also following specific guidelines or constraints, such as writing from a particular character's perspective or incorporating certain plot elements. The model's flexibility and coherence in responding to these types of prompts is a key strength.
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The TTM (TinyTimeMixer) model is a compact pre-trained model for Multivariate Time-Series Forecasting, open-sourced by IBM Research. With less than 1 Million parameters, TTM introduces the concept of the first tiny pre-trained models for Time-Series Forecasting. TTM outperforms several popular benchmarks demanding billions of parameters in zero-shot and few-shot forecasting. TTMs are lightweight forecasters, pre-trained on publicly available time series data with various augmentations. Similar models include the t5-base language model developed by Google, the switch-c-2048 Mixture of Experts model from Google, and the MiniCPM-2B-sft-bf16 model from OpenBMB. Model inputs and outputs Inputs Time series data**: The TTM model takes in time series data as input, which can have varying frequencies (e.g. 10 min, 15 min, 1 hour). Outputs Forecasts**: The TTM model outputs forecasts for the time series data, providing point estimates for future time steps. Capabilities The TTM model provides state-of-the-art zero-shot forecasts and can be easily fine-tuned for multi-variate forecasts with just 5% of the training data to be competitive. It outperforms several popular benchmarks demanding billions of parameters, including GPT4TS, LLMTime, SimMTM, Time-LLM, and UniTime. What can I use it for? The TTM model can be used for a variety of time series forecasting use cases, such as: Electricity load forecasting**: Predicting future electricity demand to aid in grid management and planning. Stock price forecasting**: Forecasting stock prices to inform investment decisions. Retail sales forecasting**: Predicting future sales to optimize inventory and staffing. The lightweight nature of the TTM model also makes it well-suited for deployment on resource-constrained devices like laptops or smartphones. Things to try One interesting aspect of the TTM model is its ability to perform well in zero-shot forecasting, without any fine-tuning on the target dataset. This can be a valuable capability when dealing with new or unfamiliar time series data, as it allows you to get started quickly without the need for extensive fine-tuning. Another thing to explore is the impact of the context length on the model's zero-shot performance. As the paper mentions, increasing the context length can lead to improved forecasting accuracy, up to a certain point. Experimenting with different context lengths and observing the results can provide valuable insights into the model's behavior.
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