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darts | mlforecast | |
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47 | 11 | |
7,159 | 693 | |
3.1% | 7.6% | |
9.1 | 8.8 | |
about 18 hours ago | 22 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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darts
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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As others have pointed out, Prophet is not a particularly good model for forecasting, and has been superseded by a multitude of other models.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]?
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gluonts VS darts - a user suggested alternative
2 projects | 13 Apr 2023
active support
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Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
Darts gives you a lot of options, including newer deep learning approaches like NBEATS and NHiTS.
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.
To be fair, Darts looks pretty good relative to forecast: https://github.com/unit8co/darts
I would generally prefer R for this kind of stuff as the experts generally write the code, but Darts seems OK and is well-tested, at the very least (haven't had a chance to use it in anger yet).
- [D] Time Series Question
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[D] Fool me once, shame on you; fool me twice, shame on me: Exponential Smoothing vs. Facebook's Neural-Prophet.
There is also a version of N-BEATS in Darts (https://github.com/unit8co/darts) that extends the original N-BEATS by * Accepting exogenous covariate time series * Being able to produce probabilistic forecasts * Working on multivariate time series (all of this out of the box, fit() / predict() style) :D
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)
GluonTS Differences: -GluonTS is written in mxnet, which reduces its adoption. In contrast, NeuralForecast is written in PyTorch. -Including new models in GluonTS tends to be challenging because mxnet 's and the library structure's learning curve are steep. PyTorch-Forecasting Differences: -NeuralForecast hosts some models from our research, including N-HiTS and Transformer-based (Autoformer, Informer, Transformer, etc.) methods specialized in long-horizon forecasting (https://arxiv.org/abs/2201.12886). -And the exogenous variables extension of N-BEATS, the NBEATSx (https://arxiv.org/abs/2104.05522). Extra Features: -NeuralForecast has a wide range of curated datasets used in research to develop and test new models, such as Tourism, M3, M4, M5, EPF, ILI, Traffic, Weather, etc. -NeuralForecast models include reasonable hyperparameter spaces to speed up hyperparameter search, based on our experience. -We include an experiment module that makes it easy to put the entire time series forecasting pipeline into production. -Finally, NeuralForecast is part of a larger ecosystem of time-series analysis and forecasting that includes feature creation (tsfeatures, https://github.com/Nixtla/tsfeatures), machine learning models (mlforecast, https://github.com/Nixtla/mlforecast) and statistical models (statsforecast, https://github.com/Nixtla/statsforecast).
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.
We know that there is no generic solution and that each dataset requires particular work; however, in addition to NeuralForecast we have other time series processing libraries for faster iteration such as MLForecast (https://github.com/Nixtla/mlforecast) and StatsForecast (https://github.com/Nixtla/statsforecast). In addition, NeuralForecast includes models that have demonstrated excellent performance in different datasets and scenarios, such as NHITS (https://arxiv.org/abs/2201.12886).
What are some alternatives?
sktime - A unified framework for machine learning with time series
pytorch-forecasting - Time series forecasting with PyTorch
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.
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
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
nixtla - Python SDK for TimeGPT, a foundational time series model
neural_prophet - NeuralProphet: A simple forecasting package
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
greykite - A flexible, intuitive and fast forecasting library
flow-forecast - Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).