TimeGEN-1
Version: 1
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Key capabilities
About this model
TimeGEN-1 is a generative pre-trained forecasting and anomaly detection model for time series data. The model excels at zero-shot forecasting by leveraging temporal correlations learnt on billions of time series.Key model capabilities
- Zero-shot forecasting for new time series without training
- Anomaly detection
- Fine-tuning capabilities on new data to improve accuracy
- Support for multivariate input
- Exogenous variables support
- Demand forecasting
Use cases
See Responsible AI for additional considerations for responsible use.Key use cases
TimeGEN-1 can produce accurate forecasts for new time series without training using only historical values and exogenous covariates as inputs. The model is optimized for forecasting and anomaly detection tasks, including demand forecasting, monitoring ordered data points to spot irregularities that may signal issues or threats, and leveraging exogenous variables as external factors that can influence forecasts.Out of scope use cases
- Transferability between Domains: Time series models trained on data from different domains, may not always perform accurately when applied to a different domain.
- Impact of Extreme Events: Extreme events (such as natural disasters, economic crises, or pandemics) can significantly impact the accuracy of time series models. These events often create patterns and trends that the model has not encountered during training, leading to poor performance.
- Data Quality and Preprocessing: The quality of the input data greatly affects the accuracy of the time series model. Issues such as missing values, outliers, and inconsistent data can lead to unreliable predictions.
- Accuracy: Artificial intelligence and machine learning are rapidly evolving fields of study. We are constantly working to improve TimeGEN-1 to make them more accurate, reliable, safe, and beneficial. Given the probabilistic nature of machine learning, the use of our Product may, in some situations, result in incorrect Output. You should always evaluate the accuracy of any Output as appropriate for your use case, including by using human review of the Output.
Pricing
Pricing is based on a number of factors, including deployment type and tokens used. See pricing details here.Technical specs
TimeGEN-1 was pretrained on 100 billion tokens of time series data from publicly available sources. The model has 500M parameters.Training cut-off date
The pretraining data has a cutoff of September 2023 but some tuning data is more recent up to March 2024.Training time
TimeGEN-1 was trained between July 2023 and October 2023.Input formats
Time series data as json or dataframes (Support for multivariate input).Output formats
Time Series data as json.Supported languages
The provider has not supplied this information.Sample JSON response
{
"timestamp": [
"2016-01-14 00:00:00",
"2016-01-15 00:00:00",
"2016-01-16 00:00:00",
"2016-01-17 00:00:00",
"2016-01-18 00:00:00",
"2016-01-19 00:00:00",
"2016-01-20 00:00:00"
],
"value": [
8.05051040649414,
7.909270763397217,
7.8442702293396,
8.21353816986084,
8.600921630859375,
8.293558120727539,
8.150216102600098
],
"input_tokens": 43,
"output_tokens": 7,
"finetune_tokens": 0
}
Model architecture
TimeGEN-1 is an auto-regressive time series model optimized for forecasting and anomaly detection tasks. The model excels at zero-shot forecasting by leveraging temporal correlations learnt on billions of time series. TimeGEN-1's parameters can be fine-tuned on new data to further improve accuracy.Long context
The provider has not supplied this information.Optimizing model performance
TimeGEN-1's parameters can be fine-tuned on new data to further improve accuracy.Additional assets
The provider has not supplied this information.Training disclosure
Training, testing and validation
TimeGEN-1 was pretrained on 100 billion tokens of time series data from publicly available sources. We used custom training libraries Nixtla's open-source libraries, Nixtla's Research Cluster and production clusters for pretraining. Fine-tuning and evaluation were also performed on third-party cloud compute.Distribution
Distribution channels
The provider has not supplied this information.More information
Responsible AI considerations
Safety techniques
The provider has not supplied this information.Safety evaluations
The provider has not supplied this information.Known limitations
- Accuracy: Artificial intelligence and machine learning are rapidly evolving fields of study. We are constantly working to improve TimeGEN-1 to make them more accurate, reliable, safe, and beneficial. Given the probabilistic nature of machine learning, the use of our Product may, in some situations, result in incorrect Output. You should always evaluate the accuracy of any Output as appropriate for your use case, including by using human review of the Output.
- Transferability between Domains: Time series models trained on data from different domains, may not always perform accurately when applied to a different domain.
- Impact of Extreme Events: Extreme events (such as natural disasters, economic crises, or pandemics) can significantly impact the accuracy of time series models. These events often create patterns and trends that the model has not encountered during training, leading to poor performance.
- Data Quality and Preprocessing: The quality of the input data greatly affects the accuracy of the time series model. Issues such as missing values, outliers, and inconsistent data can lead to unreliable predictions.
Acceptable use
Acceptable use policy
The provider has not supplied this information.Quality and performance evaluations
Source: Nixtla The provider has not supplied this information.Benchmarking methodology
Source: Nixtla The provider has not supplied this information.Public data summary
Source: Nixtla The provider has not supplied this information.Model Specifications
LicenseCustom
Training DataOctober 2023
Last UpdatedOctober 2025
ProviderNixtla
Languages1 Language