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GitHub / ramtiin / Predicting-Stock-Prices-Using-FB-Prophet
Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well. In this notebook I'm going to try forecasting Google stock price using facebook's prophet model.
Stars: 12
Forks: 1
Open Issues: 0
License: mit
Language: Jupyter Notebook
Repo Size: 483 KB
Dependencies:
0
Created: almost 3 years ago
Updated: 5 months ago
Last pushed: almost 3 years ago
Last synced: 4 months ago
Commit Stats
Commits: 4
Authors: 1
Mean commits per author: 4.0
Development Distribution Score: 0.0
More commit stats: https://commits.ecosyste.ms/hosts/GitHub/repositories/ramtiin/Predicting-Stock-Prices-Using-FB-Prophet
Topics: forecasting, machine-learning, prophet, series, time-series, trend
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