The Facebook Prophet model is an open source time series forecasting tool developed by Facebook’s data science team. Prophet is designed to make time series forecasting easy, accessible and performant. Some key features of the Prophet model include:
What is time series forecasting?
Time series forecasting is the task of predicting future values in a time series based on past observations. A time series is a set of observations ordered in time usually collected at regular intervals such as daily, weekly or monthly. Examples of time series data include stock prices, sales, website traffic and weather measurements.
Time series forecasting has many applications in business such as predicting sales, call center staffing needs, inventory planning and financial planning. In stock market analysis, it can be used to predict future stock prices which can inform investment decisions.
Why was Prophet developed?
Most time series forecasting methods require substantial expertise and care in modeling seasonalities and incorporating new types of seasonality into models. Prophet simplifies this process by decomposing time series data into three main components:
- Trend – The overall directionality of the time series (increasing, decreasing)
- Seasonality – Periodic patterns in the data (daily, weekly, yearly seasonality)
- Holidays – Irregular events that affect a time series (holidays, sports events)
Prophet automatically detects these components from the time series data. It is robust to missing data, shifts in trend and large outliers. Prophet works best with time series data that has strong seasonal trends.
How does the Prophet model work?
The Prophet procedure follows 3 main steps:
- Fitting: Prophet decomposes the time series into trend, seasonality and holidays. The model fitting uses an additive regression model with four main components:
- A piecewise linear or logistic growth curve for modeling non-periodic changes in the time series
- A yearly seasonal component modeled using Fourier series
- A weekly seasonal component using dummy variables
- A user provided list of holidays with irregular effects
- Forecasting: Given a history of time series data, Prophet forecasts future values by extrapolating the trend and seasonal components into the future.
- Visualization: The Prophet procedure includes visualization components for analyzing the fit of the model. The forecasted values can be visualized along with trend and seasonal components.
Under the hood, Prophet uses an additive regression model with regularization to fit the seasonal components. The model fitting uses Stan and Hamiltonian Monte Carlo techniques for fast and scalable training.
What are the advantages of the Prophet model?
Some of the main advantages of Facebook’s Prophet model are:
- Automated detection of seasonalities from the data
- Robust to missing data and outliers
- Performs well with minimal tuning and strong seasonal trends
- Built-in visualization tools for model analysis
- Scales well to large datasets with minimal performance degradation
- Open source and easy to use with Python and R APIs
What are some applications of Prophet for stock prediction?
Prophet can be used in several ways for modeling and predicting stock market time series data:
- Individual stock price forecasting: Predicting future price values for a company’s stock based on historical prices and trading volumes.
- Market index forecasting: Forecasting values of market indices like S&P 500 and Dow Jones Industrial Average.
- Trading signals: Using forecasts of upward or downward trend changes to generate trading signals and optimize portfolios.
- Risk management: Estimating volatility and value-at-risk metrics using predicted confidence intervals.
- Anomaly detection: Identifying irregular events by analyzing forecast errors.
Prophet’s forecasting capabilities can be combined with other techniques like sentiment analysis on news headlines to build predictive stock market models.
What are some best practices for using Prophet on stock data?
Some tips for applying Prophet forecasting to stock market time series include:
- Use higher frequency data (daily or hourly prices) for short term forecasts. Weekly or monthly data for longer term forecasts.
- Adjust for stock splits, dividends and other corporate actions before fitting.
- Remove extreme outlier values that can skew the model.
- Add custom seasonalities like day-of-week effects.
- Combine forecasts from similar stocks or market indices as features.
- Ensemble Prophet with other models (ARIMA, LSTM) for improved accuracy.
- Perform rigorous backtesting to evaluate forecast accuracy.
Example of forecasting Tesla stock prices with Prophet
Here is an example workflow for forecasting Tesla’s daily closing stock price using Prophet in Python:
- Import prophet and other libraries like Pandas and Matplotlib.
- Load historical adjusted closing prices for Tesla stock.
- Prepare the data by including a timestamp column.
- Instantiate a Prophet model object.
- Fit the model on the historical time series data.
- Set the number of periods to forecast into the future.
- Use the model to predict future stock price values.
- Plot the forecasted values along with components like trend and uncertainty intervals.
- Evaluate forecast accuracy using error metrics like RMSE, MAPE etc.
The following plot shows an example forecast for Tesla stock using two years of historical data:
The blue line shows the forecasted values while the green and red lines indicate uncertainty intervals. The black dots are the actual observed values that the model was trained on.
Strengths and weaknesses of Prophet for stock prediction
Some of the key strengths of using Prophet for stock forecasting include:
- Fast and scalable model fitting and predictions
- Intuitive forecast visualization
- Minimal tuning required compared to other methods
- Handles missing data and outliers well
Some potential weaknesses and limitations are:
- Accuracy highly dependent on seasonality assumptions
- May not capture more complex nonlinear trends
- Can underperform on irregular or noisy time series
- Limited built-in support for exogenous variables
- Confidence intervals can underestimate uncertainty
Prophet works best when stock prices follow strong seasonal patterns. It should be backtested against benchmarks on case-specific data.
Conclusion
Facebook Prophet provides an easy to use and scalable solution for stock market time series forecasting. Its automated seasonal and holiday detection along with tunable growth modeling make it well suited for predicting cyclical stock data. Prophet enables rapid testing and iteration for building forecasting models on stock data. However, its accuracy is highly dependent on seasonal trends and performance should be validated through rigorous backtesting.