tablespoon naive

full_tbsp.py

import pandas as pd
import tablespoon as tbsp
from tablespoon.data import APPL
from tablespoon.data import SEAS
from mizani.breaks import date_breaks
from plotnine import *
from datetime import datetime


# make date string a date object
df_APPLE = APPL
df_APPLE = df_APPLE.assign(ds = lambda df: pd.to_datetime(df.ds))
df_APPLE

n = tbsp.Naive()
df_n = (n.predict(df_APPLE, horizon=7*4, frequency="D", lag = 1, uncertainty_samples = 500).assign(model = 'naive'))
df_n.head()

theme_set(theme_538)
palette = ["#000000", "#ee1d52"]
df_actuals_forecasts_n = pd.concat([df_APPLE, 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



sn = tbsp.Snaive()
df_sn = sn.predict(
    SEAS, horizon=7 * 4, frequency="D", lag=7, uncertainty_samples=800
).assign(model="snaive")

df_sn.head(10)

theme_set(theme_538)
palette = ["#000000", "#ee1d52"]
df_SEAS = SEAS
df_SEAS = df_SEAS.assign(ds = lambda df: pd.to_datetime(df.ds))
df_actuals_forecasts_n = pd.concat([df_SEAS, df_sn])
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 week"))
    + theme(axis_text_x=element_text(angle=45))
    + xlab("")
    + ggtitle("Snaive (Uncertainty expands on each seasonal horizon)")
    + scale_color_manual(palette)
)
p