Time Series Forecasting
Chronos
Safetensors
t5
time series
forecasting
foundation models
pretrained models
Instructions to use amazon/chronos-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Chronos
How to use amazon/chronos-2 with Chronos:
pip install chronos-forecasting
import pandas as pd from chronos import BaseChronosPipeline pipeline = BaseChronosPipeline.from_pretrained("amazon/chronos-2", device_map="cuda") # Load historical data context_df = pd.read_csv("https://autogluon.s3.us-west-2.amazonaws.com/datasets/timeseries/misc/AirPassengers.csv") # Generate predictions pred_df = pipeline.predict_df( context_df, prediction_length=36, # Number of steps to forecast quantile_levels=[0.1, 0.5, 0.9], # Quantiles for probabilistic forecast id_column="item_id", # Column identifying different time series timestamp_column="Month", # Column with datetime information target="#Passengers", # Column(s) with time series values to predict ) - Notebooks
- Google Colab
- Kaggle
Multilingual powerhouse — testing for mobile deployment
#6
by 3morixd - opened
This model covers Hebrew, Turkish, Arabic, German, Spanish — exactly the kind of multilingual capability we need for global mobile AI.
At Dispatch AI (FZE, UAE), we're building mobile AI that works for everyone, not just English speakers. Models like this are the foundation.
We benchmark multilingual models on our 40-phone farm (Snapdragon 865) to see which ones maintain quality across languages when quantized to 4-bit. The results vary wildly — some models lose 30% quality in non-English languages after quantization.
Would love to see multilingual evaluation results at different quantization levels.
- Dispatch AI (FZE), Sharjah UAE