darts
mlforecast
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darts | mlforecast | |
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47 | 11 | |
7,272 | 713 | |
3.1% | 5.5% | |
9.1 | 8.8 | |
4 days ago | 13 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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darts
- Darts: Python lib for forecasting and anomaly detection on time series
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[D] Doubts on the implementation of LSTMs for timeseries prediction (like including weather forecasts)
Don't use an LSTM. Get up to date with SoTA methods and read the papers in the field. LSTMs are not the way forward. Read the papers I suggested. It would be very useful to come to grips with both the Time Series Repository (https://github.com/thuml/Time-Series-Library) and Darts (https://github.com/unit8co/darts) as these are widely used for research and in industry.
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Facebook Prophet: library for generating forecasts from any time series data
As others have pointed out, Prophet is not a particularly good model for forecasting, and has been superseded by a multitude of other models. If you want to do time series forecasting, I'd recommend using Darts: https://github.com/unit8co/darts. Darts implements a wide range of models and is fairly easy to use.
The problem with time series forecasting in general is that they make a lot of assumptions on the shape of your data, and you'll find you're spending a lot of time figuring out mutating your data. For example, they expect that your data comes at a very regular interval. This is fine if it's, say, the data from a weather station. This doesn't work well in clinical settings (imagine a patient admitted into the ER -- there is a burst of data, followed by no data).
That said, there's some interesting stuff out there that I've been experimenting with that seems to be more tolerant of irregular time series and can be quite useful. If you're interested in exchanging ideas, drop me a line (email in my profile).
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Elevate Your Python Skills: Machine Learning Packages That Transformed My Journey as ML Engineer
3. darts
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Aeon: A unified framework for machine learning with time series
Looking forward to checking this out! How does this compare with darts[1]?
[1] https://unit8co.github.io/darts/
- [D] Hybrid forecasting framework ARIMA-LSTM
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[D] Do any of you have experience using Darts for forecasting?
Darts is an open-source Python library by Unit8 for easy handling, pre-processing, and forecasting of time series. It contains an array of models, from standard statistical models such as ARIMA to deep neural networks. https://unit8co.github.io/darts/
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gluonts VS darts - a user suggested alternative
2 projects | 13 Apr 2023
active support
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A Simple Guide to Feature Engineering in the Forecast Menu
The new Forecast menu, featuring the open-source Darts Time Series library, offers script-friendly functionality. It's also easy to use. Don't have any data to load yet? Enter through the Stocks or Crypto menus.
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Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
I would recommend Darts in Python [1]. It's easy to use (think fit()/predict()) and includes
* Statistical models (ETS, (V)ARIMA(X), etc)
* ML models (sklearn models, LGBM, etc)
* Many recent deep learning models (N-BEATS, TFT, etc)
* Seamlessly works on multi-dimensional series
* Models can be trained on multiple series
* Many models offer rich support for probabilistic forecasts
* Model evaluation is easy: Darts has many metrics, offers backtest etc
* Deep learning scales to large datasets, using GPUs, TPUs, etc
* There's even now an explainability module for some of the models - showing you what matters for computing the forecasts
* (coming soon): an anomaly detection module :)
* (also, it even include FB Prophet if you really want to use it)
Warning: I'm probably biased because I'm Darts creator.
[1] https://github.com/unit8co/darts
mlforecast
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Sales forecast for next two years
MLForecast
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Demand Planning
Alternatively you could try out their mlforecast package which 'featurizes' the time pieces to fit with things like LightGBM: https://github.com/Nixtla/mlforecast
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Recommendations for books on working with time series/forecasting problems?
- https://nixtla.github.io/mlforecast/
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XGBoost for time series
Leaving these two repos here for anyone interested in trying decision tree regression or statistical forecasting baselines: - https://nixtla.github.io/mlforecast/ - https://github.com/Nixtla/statsforecast
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Time series modeling using ARIMA and XGBoost. Intro to free time series modeling resources
In Python you can use the https://nixtla.github.io/mlforecast library for example, it makes the feature engineering, evaluation and cross validation trivial.
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Time series forecasting model predicts increasing number for target variable when the actual values are zeroes
You might want to take a look to this library: MLForecast.
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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)
We are already working on the comparison. For the moment, the blog shows that another of our libraries, MLForecast (https://github.com/Nixtla/mlforecast), has an excellent performance in this use case.
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Automated Time Series Processing and Forecasting
We missed that, sorry. At the moment, for forecasting the pipeline uses the mlforecast library (https://github.com/nixtla/mlforecast) that builds upong Sckilearn .xgboos and lightbmg .
What are some alternatives?
sktime - A unified framework for machine learning with time series
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
pytorch-forecasting - Time series forecasting with PyTorch
tsfeatures - Calculates various features from time series data. Python implementation of the R package tsfeatures.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Kats - Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
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
nixtla - Python SDK for TimeGPT, a foundational time series model