Extended Example

This example includes the following

1. adding missing dates
2. forward fill of NA
3. backward fill of NA
4. Using many forecast methods
5. making many plots

import datetime as dt

import numpy as np
import pandas as pd
import pandas_datareader as pdr
import tablespoon as tbsp
from mizani.breaks import date_breaks
from plotnine import *


# pull Apple open stock price
# columns must have the columns "ds" and "y"
# this time series is at the daily level. the seasonality is 7 days.
start_date = dt.datetime(2021, 8, 1)
end_date = dt.datetime(2022, 1, 1)
df = (pdr.get_data_yahoo("AAPL", start_date, end_date)
        .loc[:,['Open' ]]
        .reset_index()
        .rename(columns = {'Date': 'ds', 'Open': 'y'})
        .assign(y=lambda df: np.log(df.y))
    )

# It is common for time series data to have missing dates. 
# We need to fill in the missing values.
df_date_range = pd.DataFrame(pd.date_range(start=start_date, end=end_date), columns=['ds'])
df_complete_dates = df_date_range.merge(df, how='left', on='ds')
df_filled_forward = df_complete_dates.fillna(method = 'ffill') # fill NA with last valid value 
df_filled = df_filled_forward.fillna(method =  'bfill') # fill starting missing values with next valid value

# Snaive model
sn = tbsp.Snaive()
df_sn = (sn.predict(df_filled, horizon=7, frequency="D", lag = 7, uncertainty_samples = 8000).assign(model = 'snaive'))

# Complete Data is Required: Models Error when time series is missing dates 
n = tbsp.Naive()
df_n = (n.predict(df_filled, horizon=7*4, frequency="D", lag = 1, uncertainty_samples = 8000).assign(model = 'naive'))

# Complete Data is Required: Models Error when time series is missing dates 
m = tbsp.Mean()
df_m = (m.predict(df_filled, horizon=7*4, frequency="D", lag = 1, uncertainty_samples = 8000).assign(model = 'mean'))

# make some nice colors for plots
theme_set(theme_538)
palette = ["#000000", "#ee1d52"]

df_actuals_forecasts_sn = pd.concat([df_filled, df_sn])
p = (
    ggplot(df_actuals_forecasts_sn, aes(x="ds", y="y"))
    + geom_line(aes(y = 'y'), color = palette[0])
    + geom_point(aes(y = 'y_sim'), color = palette[1], size = 0.1, alpha = 0.1)
    + scale_x_datetime(breaks=date_breaks("1 month"))
    + theme(axis_text_x=element_text(angle=45))
    + xlab("")
    + ggtitle("Stock Price (Snaive)")
    + scale_color_manual(palette)
)
p.save(filename="forecasts_sn.jpg", width=14, height=3)

df_actuals_forecasts_n = pd.concat([df_filled, df_n])
p = (
    ggplot(df_actuals_forecasts_n, aes(x="ds", y="y"))
    + geom_line(aes(y = 'y'), color = palette[0])
    + geom_point(aes(y = 'y_sim'), color = palette[1], size = 0.1, alpha = 0.1)
    + scale_x_datetime(breaks=date_breaks("1 month"))
    + theme(axis_text_x=element_text(angle=45))
    + xlab("")
    + ggtitle("Stock Price (Naive)")
    + scale_color_manual(palette)
)
p.save(filename="forecasts_n.jpg", width=14, height=3)

df_actuals_forecasts_m = pd.concat([df_filled, df_m])
p = (
    ggplot(df_actuals_forecasts_m, aes(x="ds", y="y"))
    + geom_line(aes(y = 'y'), color = palette[0])
    + geom_point(aes(y = 'y_sim'), color = palette[1], size = 0.1, alpha = 0.1)
    + scale_x_datetime(breaks=date_breaks("1 month"))
    + theme(axis_text_x=element_text(angle=45))
    + xlab("")
    + ggtitle("Stock Price (Mean)")
    + scale_color_manual(palette)
)
p.save(filename="forecasts_m.jpg", width=14, height=3)