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.

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This helps individual investors or financial analysts predict future stock prices by analyzing historical daily stock data. It takes in past stock price movements, including yearly, weekly, and daily patterns, and outputs a forecast for how the stock might perform in the future. Anyone managing their own investments or performing market analysis would find this useful.

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Use this if you need to forecast stock prices for highly seasonal stocks with a good amount of historical data, and you want a model that handles missing information and unusual spikes well.

Not ideal if you are looking for a complex quantitative trading strategy or need to forecast assets without clear seasonal patterns.

stock-forecasting personal-investing financial-analysis market-prediction
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Jupyter Notebook

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MIT

Last pushed

Jun 05, 2021

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