def q06_sarima_predictor(path):
tss, tss_valid = q05_sarima_model(path)
mod = sarimax.SARIMAX(tss.Sales,
trend='n',
order=(1, 1, 1),
seasonal_order=(1, 1, 1, 12))
results = mod.fit(disp=-1)
## Forecasting
pred = pd.DataFrame(results.forecast(len(tss_valid)))
pred.index = tss_valid.index
## Measuring error.
measure = math.pow(mean_squared_error(tss_valid.values, pred.values), 0.5)
return pred, measure
def q06_sarima_predictor(path):
tss, tss_valid = q05_sarima_model(path)
# Model
model = sarimax.SARIMAX(tss['Sales'],
trend='n',
order=(1, 1, 1),
seasonal_order=(1, 1, 1, 12))
results = model.fit(disp=-1)
# Forecasting
y_pred = pd.DataFrame(results.forecast(len(tss_valid)))
y_pred.index = tss_valid.index
# Evaluation metric-rmse
rmse = math.pow(mean_squared_error(tss_valid.values, y_pred.values), 0.5)
return y_pred, rmse
# %load q06_sarima_predictor/build.py
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import seaborn as sns
import matplotlib.pyplot as plt
plt.switch_backend('agg')
from statsmodels.tsa.statespace import sarimax
import math
from sklearn.metrics import mean_squared_error
#import sys
#sys.path.append('./')
path = 'data/perrin-freres-monthly-champagne.csv'
from greyatomlib.time_series_101_project.q05_sarima_model.build import q05_sarima_model
tss, tss_valid = q05_sarima_model(path)
'write your solution here'
def q06_sarima_predictor(path):
tss, tss_valid = q05_sarima_model(path)
mod = sarimax.SARIMAX(tss.Sales,
trend='n',
order=(1, 1, 1),
seasonal_order=(1, 1, 1, 12))
results = mod.fit(disp=-1)
## Forecasting
pred = pd.DataFrame(results.forecast(len(tss_valid)))
pred.index = tss_valid.index
def q06_sarima_predictor(path):
tss, tss_valid = q05_sarima_model(path)
"write your solution here"