Create funcoes_modelos

funcoes_modelos

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import urllib.request
+import json
+import plotly.express as px
+import matplotlib.pyplot as plt
+import yfinance as yf
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from datetime import datetime
+import statsmodels.api as sm
+
+from sklearn.linear_model import LinearRegression
+from statsmodels.tsa.seasonal import seasonal_decompose
+from sklearn.model_selection import TimeSeriesSplit
+from sklearn.metrics import mean_squared_error
+
+from statsforecast.models import HistoricAverage
+from statsforecast.models import Naive
+from statsforecast.models import RandomWalkWithDrift
+from statsforecast.models import SeasonalNaive
+from statsforecast.models import SimpleExponentialSmoothing
+from statsforecast.models import HoltWinters
+from statsforecast.models import AutoARIMA
+from statsforecast.models import ARIMA
+from statsforecast.models import GARCH
+from statsforecast.models import ARCH
+
+from statsmodels.graphics.tsaplots import plot_pacf
+from statsmodels.graphics.tsaplots import plot_acf
+
+from scipy.stats import shapiro
+from datetime import datetime
+import matplotlib.pyplot as plt
+from meteostat import Point, Daily
+
+from statsmodels.graphics.tsaplots import plot_pacf
+from statsmodels.graphics.tsaplots import plot_acf
+from statsmodels.tsa.statespace.sarimax import SARIMAX
+from statsmodels.tsa.holtwinters import ExponentialSmoothing
+from statsmodels.tsa.stattools import adfuller
+import matplotlib.pyplot as plt
+from tqdm import tqdm_notebook
+from itertools import product
+
+
+import warnings
+warnings.filterwarnings('ignore')
+
+def read_data():
+        # Set time period
+    start = datetime(2010, 1, 1)
+    end = pd.to_datetime(datetime.now().strftime("%Y-%m-%d"))
+    # Create Point for Vancouver, BC
+    vancouver = Point(49.2497, -123.1193, 70)
+    #campinas = Point(-22.9056, -47.0608, 686)
+    #saopaulo = Point(-23.5475, -46.6361, 769)
+
+    # Get daily data for 2018
+    data = Daily(vancouver, start, end)
+    data = data.fetch()
+    data = data[['tavg', 'prcp']]
+
+    return data
+
+data = read_data()
+returns = data['tavg']
+
+def montar_dataframe_temp(returns):
+
+  temp = pd.DataFrame(returns)
+  temp['precip_ontem'] = data['prcp'].shift(1)
+  temp['precip_media_semana'] = temp['precip_ontem'].rolling(7).mean()
+  temp = temp.dropna(axis = 0)
+  return temp
+
+def predict_ARIMA_GARCH(models, temp_train, n):
+  model = models[0]
+  model2 = models[1]
+
+  sarimax = sm.tsa.statespace.SARIMAX(temp_train['tavg'] , order=(1,0,1),
+                                enforce_stationarity=False, enforce_invertibility=False, freq='D').fit()
+
+  resid = sarimax.resid.values
+
+  garch = model2.fit(resid)
+
+  pred1 = sarimax.forecast(n, exog = return_exog(temp_train, n).values).values
+  pred2 = garch.predict(n)
+
+  predictions = pred1 - pred2['mean']
+
+  return predictions
+def return_exog(temp, n):
+
+  exog = pd.DataFrame(columns = ['precip_ontem', 'precip_media_semana'])
+
+  exog['precip_ontem'] = np.ones(n)*temp.iloc[-1]['precip_ontem']
+
+  exog['precip_media_semana'] = np.ones(n)*temp.iloc[-1]['precip_media_semana']
+
+  return exog