nixtla
chronos-forecasting
| nixtla | chronos-forecasting | |
|---|---|---|
| 8 | 3 | |
| 1,445 | 1,680 | |
| 8.9% | 22.7% | |
| 9.5 | 6.8 | |
| 2 days ago | 19 days ago | |
| Jupyter Notebook | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
nixtla
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Chronos: Learning the Language of Time Series
I do not have a horse in the race, but it is interesting to see open source comparisons to traditional timeseries strategies: https://github.com/Nixtla/nixtla/tree/main/experiments/amazo...
In general, the M-Competitions (https://forecasters.org/resources/time-series-data/), the olympics of timeseries forecasting, have proven frustrating for ML methods... linear models do shockingly well and the ML models that have won, generally seem to be variants of older tree-based methods (ie. LightGBM is a favorite).
Will be interesting to see whether the Transformer architecture ends up making real progress here.
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[P] Beware of false (FB-)Prophets: Introducing the fastest implementation of auto ARIMA [ever].
Yes, for example we have this paper in long-horizon settings using our library NeuralForecast and this experiment with other of our libraries MLForecast, both of them outperforming autoarima.
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[P] Deep Learning for time series forecasting (neuralforecast, python package)
Here we did some comparison with prophet in the zillow real-state dataset https://github.com/Nixtla/nixtla/tree/main/utils/experiments/zillow-prophet
- Is linear regression better than prophet? Zillow benchmark
- Prophet vs. Linear Regression on Real Estate: The Zillow Case
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Automated Time Series Processing and Forecasting
Users can deploy the pipeline in their cloud quickly. We use terraform (https://github.com/Nixtla/nixtla/tree/main/iac/terraform/aws), so it is very simple to deploy the pipeline on AWS. We are working to release versions of terraform on other clouds such as Azure and Google Cloud.
chronos-forecasting
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Financial Market Applications of LLMs
There were some developments using LLMs in the timeseries domain which caught my attention.
I toyed with the Chronos forecasting toolkit [1], and the results were predictably off by wild margins [2]
What really caught my eye though was the "feel" of the predicted timeseries -- this is the first time I've seen synthetic timeseries that look like the real thing. Stock charts have a certain quality to them, once you've been looking at them long enough, you can tell more often than not whether some unlabeled data is a stock price timeseries or not. It seems the chronos LLM was able to pick up on that "nature" of the price movement, and replicate it in its forecasts. Impressive!
1: https://github.com/amazon-science/chronos-forecasting
2: https://imgur.com/a/hTRQ38d
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Chronos: Learning the Language of Time Series
https://github.com/amazon-science/chronos-forecasting
- Chronos: Pretrained (Language) Models for Probabilistic Time Series Forecasting
What are some alternatives?
darts - A python library for user-friendly forecasting and anomaly detection on time series.
gluonts - Probabilistic time series modeling in Python
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
meta-prompting - Official implementation of BGPT @ ICLR 2024 paper "Meta Prompting for AI Systems" (https://arxiv.org/abs/2311.11482)
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
pytorch-forecasting - Time series forecasting with PyTorch
tsai - Time series Timeseries Deep Learning Machine Learning Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
tsfeatures - Calculates various features from time series data. Python implementation of the R package tsfeatures.
nixtlats - Deep Learning for Time Series Forecasting.
TGLSTM - Pytorch implementation of LSTM for irregular time series
mlforecast - Scalable machine 🤖 learning for time series forecasting.
eland - Python Client and Toolkit for DataFrames, Big Data, Machine Learning and ETL in Elasticsearch