marcopeix

# Drop irrelevant columns
cols_to_drop = ['PT08.S1(CO)', 'C6H6(GT)', 'PT08.S2(NMHC)', 'PT08.S4(NO2)', 'PT08.S5(O3)', 'T', 'RH', 'AH']

weekly_data = weekly_data.drop(cols_to_drop, axis=1)

# Import Prophet
from fbprophet import Prophet
import logging

logging.getLogger().setLevel(logging.ERROR)

marcopeix

# Make dates actual dates
data['Date'] = pd.to_datetime(data['Date'])

# Convert measurements to floats
for col in data.iloc[:,2:].columns:
    if data[col].dtypes == object:
        data[col] = data[col].str.replace(',', '.').astype('float')

# Compute the average considering only the positive values
def positive_average(num):

marcopeix

import warnings
warnings.filterwarnings('ignore')

import numpy as np
import pandas as pd
from scipy import stats
import statsmodels.api as sm
import matplotlib.pyplot as plt

%matplotlib inline

marcopeix

# Make a dataframe containing actual and predicted prices
comparison = pd.DataFrame({'actual': [18.93, 19.23, 19.08, 19.17, 19.11, 19.12],
                          'predicted': [18.96, 18.97, 18.96, 18.92, 18.94, 18.92]}, 
                          index = pd.date_range(start='2018-06-05', periods=6,))


#Plot predicted vs actual price

plt.figure(figsize=(17, 8))
plt.plot(comparison.actual)

marcopeix

#Set initial values and some bounds
ps = range(0, 5)
d = 1
qs = range(0, 5)
Ps = range(0, 5)
D = 1
Qs = range(0, 5)
s = 5

#Create a list with all possible combinations of parameters

marcopeix

def tsplot(y, lags=None, figsize=(12, 7), syle='bmh'):
    
    if not isinstance(y, pd.Series):
        y = pd.Series(y)
        
    with plt.style.context(style='bmh'):
        fig = plt.figure(figsize=figsize)
        layout = (2,2)
        ts_ax = plt.subplot2grid(layout, (0,0), colspan=2)
        acf_ax = plt.subplot2grid(layout, (1,0))

marcopeix

def double_exponential_smoothing(series, alpha, beta):

    result = [series[0]]
    for n in range(1, len(series)+1):
        if n == 1:
            level, trend = series[0], series[1] - series[0]
        if n >= len(series): # forecasting
            value = result[-1]
        else:
            value = series[n]

marcopeix

def exponential_smoothing(series, alpha):

    result = [series[0]] # first value is same as series
    for n in range(1, len(series)):
        result.append(alpha * series[n] + (1 - alpha) * result[n-1])
    return result
  
def plot_exponential_smoothing(series, alphas):
 
    plt.figure(figsize=(17, 8))

marcopeix

def plot_moving_average(series, window, plot_intervals=False, scale=1.96):

    rolling_mean = series.rolling(window=window).mean()
    
    plt.figure(figsize=(17,8))
    plt.title('Moving average\n window size = {}'.format(window))
    plt.plot(rolling_mean, 'g', label='Rolling mean trend')
    
    #Plot confidence intervals for smoothed values
    if plot_intervals:

marcopeix

# Plot closing price

plt.figure(figsize=(17, 8))
plt.plot(data.CLOSE)
plt.title('Closing price of New Germany Fund Inc (GF)')
plt.ylabel('Closing price ($)')
plt.xlabel('Trading day')
plt.grid(False)
plt.show()