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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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nixtla
TimeGPT-1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code 🚀.
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SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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Puts Debuggerer
Ruby library for improved puts debugging, automatically displaying bonus useful information such as source line number and source code.
Hi, are you referring to the link in the paper? It is based on our NeuralForecast library (https://github.com/Nixtla/neuralforecast). You can install all our libraries using pip and conda, and the API is quite similar to sklearn (train and forecast). :)
Please check it out and give us a star if you like it https://github.com/Nixtla/statsforecast.
Inspired by this, we translated Hyndman's auto.arima code from R and compiled it using the numba library. The result is faster than the original implementation and more accurate than prophet .
To name a few: https://github.com/jdb78/pytorch-forecasting, https://github.com/unit8co/darts, https://github.com/Nixtla/neuralforecast
To name a few: https://github.com/jdb78/pytorch-forecasting, https://github.com/unit8co/darts, https://github.com/Nixtla/neuralforecast
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.
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.
It could be interesting, but make it a proper Python package and follow the sklearn interface. It requires very little effort (once you know how). It is not inviting, if it is installed by custom commands and then only offers an opinionated evaluation on self-selected datasets. It would be much more convincing if one could do pip install git+https://github.com/... and then use .fit and .predict methods which everyone is familiar with. People would test it on their own data sets. Testing on the paper's dataset does not mean much - just as it didn't for Prophet.
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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)
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[D] Hybrid forecasting framework ARIMA-LSTM
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[D] Do any of you have experience using Darts for forecasting?
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gluonts VS darts - a user suggested alternative
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