Deep_XF
neural_prophet
Deep_XF | neural_prophet | |
---|---|---|
3 | 5 | |
110 | 3,644 | |
- | - | |
10.0 | 8.6 | |
over 1 year ago | 21 days ago | |
Jupyter Notebook | Python | |
- | MIT License |
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Deep_XF
neural_prophet
- Facebook Prophet: library for generating forecasts from any time series data
- Time series analysis of Bitcoin price in Python with fbprophet ?!
- 14 September 2021 - Daily Chat Thread
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[D] Stock prediction using lstm(plz help)
NeuralProphet
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Financial time-series data forecasting - any other tools besides Prophet?
Neural Prophet: https://github.com/ourownstory/neural_prophet
What are some alternatives?
modeltime - Modeltime unlocks time series forecast models and machine learning in one framework
darts - A python library for user-friendly forecasting and anomaly detection on time series.
Autoformer - About Code release for "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting" (NeurIPS 2021), https://arxiv.org/abs/2106.13008
scikit-hts - Hierarchical Time Series Forecasting with a familiar API
wb_gdp_predict - Predicting next year's GDP using ML (Python)
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.
Auto_TS - Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Created by Ram Seshadri. Collaborators welcome.
orbit - A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.
lockdowndates - Retrieve the dates of the restrictions imposed by governments in countries around the world during the covid-19 pandemic.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
BrewPOTS - The tutorials for PyPOTS.
sysidentpy - A Python Package For System Identification Using NARMAX Models