Time series forecast can be used in a wide variety of applications such as Budget Forecasting, Stock Market Analysis, etc. But as useful it is also challenging to forecast the correct projections, Thus can’t be easily automated because of the underlying assumptions and factors. The analysts who produced accurate forecasts are also rare, and there is a big market available for them because it requires a substantial understanding of statistics and data analysis and has prior experience of producing time series forecasting.
Facebook open-sourced its time-series forecasting tool called Prophet in 2017 which produced accurate forecasts as produced by skilled analysts with a minimum amount of human efforts. The Facebook prophet is available in the form of API in Python and R/
How Prophet Works:
Facebook Prophet using Additive Regressive models using the following four components:
y(t) = g(t) + s(t) + h(t) + \epsilon_t
- g(t): A piecewise linear or logistic growth curve trend. Prophet automatically detects changes in trends by selecting change points from the data.
- s(t): A yearly seasonal component modeled using the Fourier series and weekly seasonal component using dummy variable
- h(t): A user-provided list of important holidays.
- et: Error term used by the prophet.
Advantages of Facebook Prophet:
the prophet is optimized for business-related problems that are encountered at Facebook, it has the following characteristics:
- The Facebook prophet is as accurate as a skilled analyst and can generate results in seconds
- Facebook prophet requires minimal data processing and can deal with several outliers and null values.
- User can add seasonality and holidays values manually, this can help easily integrate the particular domain knowledge.
In this post, we will use Facebook prophet with Python. We try to forecast the share price of Amazon Stock (from 2019-2020) using the share price data from (2015-2019).
Implementation:
- For this post, we will be using Amazon Stock Price data, it can be downloaded from yahoo finance website.
- First, we need to install the fbprophet tool, it can be installed with the following command in python.
Requirement already satisfied: fbprophet in /usr/local/lib/python3.6/dist-packages (0.6) Requirement already satisfied: Cython>=0.22 in /usr/local/lib/python3.6/dist-packages (from fbprophet) (0.29.21) Requirement already satisfied: cmdstanpy==0.4 in /usr/local/lib/python3.6/dist-packages (from fbprophet) (0.4.0) Requirement already satisfied: pystan>=2.14 in /usr/local/lib/python3.6/dist-packages (from fbprophet) (2.19.1.1) Requirement already satisfied: numpy>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from fbprophet) (1.18.5) Requirement already satisfied: pandas>=0.23.4 in /usr/local/lib/python3.6/dist-packages (from fbprophet) (1.0.5) Requirement already satisfied: matplotlib>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from fbprophet) (3.2.2) Requirement already satisfied: LunarCalendar>=0.0.9 in /usr/local/lib/python3.6/dist-packages (from fbprophet) (0.0.9) Requirement already satisfied: convertdate>=2.1.2 in /usr/local/lib/python3.6/dist-packages (from fbprophet) (2.2.1) Requirement already satisfied: holidays>=0.9.5 in /usr/local/lib/python3.6/dist-packages (from fbprophet) (0.9.12) Requirement already satisfied: setuptools-git>=1.2 in /usr/local/lib/python3.6/dist-packages (from fbprophet) (1.2) Requirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.6/dist-packages (from fbprophet) (2.8.1) Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.23.4->fbprophet) (2018.9) Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.0.0->fbprophet) (0.10.0) Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.0.0->fbprophet) (1.2.0) Requirement already satisfied: pyparsing!=2.0.4, !=2.1.2, !=2.1.6, >=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.0.0->fbprophet) (2.4.7) Requirement already satisfied: ephem>=3.7.5.3 in /usr/local/lib/python3.6/dist-packages (from LunarCalendar>=0.0.9->fbprophet) (3.7.7.1) Requirement already satisfied: pymeeus<=1, >=0.3.6 in /usr/local/lib/python3.6/dist-packages (from convertdate>=2.1.2->fbprophet) (0.3.7) Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from holidays>=0.9.5->fbprophet) (1.12.0)
- Now, we need to import fbprophet and some other modules for data processing and plotting. We will use mean squared error and mean absolute error as our metrics.
Code:
python3
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import fbprophet as fbp
from sklearn.metrics import mean_squared_error, mean_absolute_error
# Use fivethirtyeight plot styleplt.style.use('fivethirtyeight')
|
- Now, we will read data from CSV file and put them into a pandas data frame.
Code:
python3
# We will be using amazon share price data which can be downloaded from YAHOO finance website.df = pd.read_csv('sample_data / AMZN.csv')
df.head() |
Output:
Date Open High Low Close Adj Close Volume 0 2015-07-21 487.899994 488.880005 482.549988 488.000000 488.000000 3181800 1 2015-07-22 485.989990 492.500000 484.899994 488.269989 488.269989 3114900 2 2015-07-23 491.660004 491.660004 475.700012 482.179993 482.179993 9374400 3 2015-07-24 578.989990 580.570007 529.349976 529.419983 529.419983 21909400 4 2015-07-27 527.750000 544.950012 526.599976 531.409973 531.409973 7491000
- Since we need only two columns date and adjusted close price, so, we subset the original dataset to get these columns. Since the data is required in a prophet in the form of two columns named ds (for date column) and y (for data column).
Code:
python3
# add two columnsin dataframe having values as Date and Adj Closedf[['ds', 'y']] = df[['Date', 'Adj Close']]
# Subset two columns from data framedf = df[['ds', 'y']]
df.head() |
Output:
ds y 0 2015-07-21 488.000000 1 2015-07-22 488.269989 2 2015-07-23 482.179993 3 2015-07-24 529.419983 4 2015-07-27 531.409973
- Now, we split the data frame into train and test data, we will be using 4 years of data for training and a year of data for test purpose.
Code:
python3
# split data frame into two parts train and testsplit_date = "2019-07-21"
df_train = df.loc[df.ds <= split_date].copy()
df_test = df.loc[df.ds > split_date].copy()
|
- Now, we instantiate the Facebook prophet API, this prophet API works similar to scikit-learn. It uses the fit function to fit the dataset into the model and predict function to forecast future values.
Code:
python3
# Instantiate prophet model = fbp.Prophet()
# fit the training data model.fit(df_train) |
- Now, we use predict function to forecast the share price for next 1 year.
Code:
python3
forecast = model.predict(df_test)
forecast.tail() |
Output:
ds trend yhat_lower yhat_upper trend_lower trend_upper additive_terms additive_terms_lower additive_terms_upper weekly weekly_lower weekly_upper yearly yearly_lower yearly_upper multiplicative_terms multiplicative_terms_lower multiplicative_terms_upper yhat 247 2020-07-14 1992.862925 1479.553875 2566.925238 1403.962381 2483.045869 93.536964 93.536964 93.536964 -25.535936 -25.535936 -25.535936 119.072900 119.072900 119.072900 0.0 0.0 0.0 2086.399889 248 2020-07-15 1993.215324 1485.368711 2575.314593 1401.835761 2485.386736 97.405883 97.405883 97.405883 -25.138654 -25.138654 -25.138654 122.544537 122.544537 122.544537 0.0 0.0 0.0 2090.621207 249 2020-07-16 1993.567723 1484.197262 2589.201052 1399.740456 2487.727602 100.236350 100.236350 100.236350 -25.549805 -25.549805 -25.549805 125.786155 125.786155 125.786155 0.0 0.0 0.0 2093.804073 250 2020-07-17 1993.920121 1478.807958 2617.093500 1397.645151 2490.068469 99.309824 99.309824 99.309824 -29.445843 -29.445843 -29.445843 128.755666 128.755666 128.755666 0.0 0.0 0.0 2093.229945 251 2020-07-20 1994.977318 1475.034301 2618.609494 1389.089958 2497.091069 104.649308 104.649308 104.649308 -31.050560 -31.050560 -31.050560 135.699868 135.699868 135.699868 0.0 0.0 0.0 2099.626626
- As we can see this column contains the date column, predict share price (y_hat), lower and upper estimates of it, trend components, seasonal components (weekly and yearly).
Code:
python3
model.plot(forecast) |
Output:
- The command will plot the components of the prophet such as: trend line, weekly and yearly seasonality.
Code:
python3
# plot graphs of different components:model.plot_components(forecast) |
Output:
- Now, we calculate the mean square error and mean absolute error for the forecasted data.
Code:
python3
# codeprint("Mean Squared Error (MSE):", mean_squared_error(y_true = df_test["y"], y_pred = forecast['yhat']))
print("Mean Absolute Error (MAE):", mean_absolute_error(y_true = df_test["y"], y_pred = forecast['yhat']))
|
Output:
Mean Squared Error (MSE): 121417.80253038534 Mean Absolute Error (MAE): 246.57694290710793
- Now, we calculate the mean absolute percentage error of our forecast, because it gives a better idea about how accurate our prediction is
Code:
python3
def mean_abs_perc_err(y_true, y_pred):
return np.mean(np.abs((y_true - y_pred) / y_true)) * 100
print("Mean Absolute % Error (MAPE): ", mean_abs_perc_err(y_true = np.asarray(df_test["y"]), y_pred = np.asarray(forecast['yhat'])))
|
Output:
Mean Absolute % Error (MAPE): 10.693787212532687
References:
Article Tags :
Recommended Articles