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time_series_analyzer.py
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time_series_analyzer.py
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from datetime import date
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import r2_score, median_absolute_error, mean_absolute_error
from sklearn.metrics import median_absolute_error, mean_squared_error, mean_squared_log_error
from scipy.optimize import minimize
import statsmodels.tsa.api as smt
import statsmodels.api as sm
from tqdm import tqdm_notebook
from itertools import product
import warnings
class TimeSeriesAnalyzer:
def __init__(self, ticker) -> None:
self.ticker = ticker
sns.set()
warnings.filterwarnings('ignore')
return
def mean_absolute_percentage_error(self, actual, predicted) -> float:
return np.mean(np.abs((actual - predicted) / actual)) * 100
def read_data(self) -> pd.DataFrame:
path = f'./data_retriever_storage/prices/{self.ticker}_prices.csv'
data = pd.read_csv(path, index_col=['Date'], parse_dates=['Date'])
return data
def plot_moving_average(self, 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:
mae = mean_absolute_error(series[window:], rolling_mean[window:])
deviation = np.std(series[window:] - rolling_mean[window:])
lower_bound = rolling_mean - (mae + scale * deviation)
upper_bound = rolling_mean + (mae + scale * deviation)
plt.plot(upper_bound, 'r--', label='Upper bound / Lower bound')
plt.plot(lower_bound, 'r--')
plt.plot(series[window:], label='Actual values')
plt.legend(loc='best')
plt.grid(True)
plt.show()
def exponential_smoothing(self, 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(self, series, alphas):
plt.figure(figsize=(17, 8))
for alpha in alphas:
plt.plot(self.exponential_smoothing(series, alpha), label="Alpha {}".format(alpha))
plt.plot(series.values, "c", label = "Actual")
plt.legend(loc="best")
plt.axis('tight')
plt.title("Exponential Smoothing")
plt.grid(True)
def double_exponential_smoothing(self, 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]
last_level, level = level, alpha * value + (1 - alpha) * (level + trend)
trend = beta * (level - last_level) + (1 - beta) * trend
result.append(level + trend)
return result
def plot_double_exponential_smoothing(self, series, alphas, betas):
plt.figure(figsize=(17, 8))
for alpha in alphas:
for beta in betas:
plt.plot(self.double_exponential_smoothing(series, alpha, beta), label="Alpha {}, beta {}".format(alpha, beta))
plt.plot(series.values, label = "Actual")
plt.legend(loc="best")
plt.axis('tight')
plt.title("Double Exponential Smoothing")
plt.grid(True)
def tsplot(self, 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))
pacf_ax = plt.subplot2grid(layout, (1,1))
y.plot(ax=ts_ax)
p_value = sm.tsa.stattools.adfuller(y)[1]
ts_ax.set_title('Time Series Analysis Plots\n Dickey-Fuller: p={0:.5f}'.format(p_value))
smt.graphics.plot_acf(y, lags=lags, ax=acf_ax)
smt.graphics.plot_pacf(y, lags=lags, ax=pacf_ax)
plt.tight_layout()
return p_value
# Train many SARIMA models to find the best set of parameters
def optimize_SARIMA(self, parameters_list, d, D, s, data):
"""
Return dataframe with parameters and corresponding AIC
parameters_list - list with (p, q, P, Q) tuples
d - integration order
D - seasonal integration order
s - length of season
"""
results = []
best_aic = float('inf')
for param in tqdm_notebook(parameters_list):
try: model = sm.tsa.statespace.SARIMAX(data.Close, order=(param[0], d, param[1]),
seasonal_order=(param[2], D, param[3], s)).fit(disp=-1)
except:
continue
aic = model.aic
#Save best model, AIC and parameters
if aic < best_aic:
best_model = model
best_aic = aic
best_param = param
results.append([param, model.aic])
result_table = pd.DataFrame(results)
result_table.columns = ['parameters', 'aic']
#Sort in ascending order, lower AIC is better
result_table = result_table.sort_values(by='aic', ascending=True).reset_index(drop=True)
return result_table
def plot_SARIMA(self, series, model, n_steps):
"""
Plot model vs predicted values
series - dataset with time series
model - fitted SARIMA model
n_steps - number of steps to predict in the future
"""
s = 1
d = 3
data = series.copy().rename(columns = {'Close': 'actual'})
drop_cols = ["Open", "High", "Low", "Adj Close", "Volume"]
data.drop(drop_cols, axis=1, inplace=True)
data['arima_model'] = model.fittedvalues
#Make a shift on s+d steps, because these values were unobserved by the model due to the differentiating
data['arima_model'][:s+d] = np.NaN
#Forecast on n_steps forward
forecast = model.predict(start=data.shape[0], end=data.shape[0] + n_steps)
forecast = data.arima_model.append(forecast)
#Calculate error
error = self.mean_absolute_percentage_error(data['actual'][s+d:], data['arima_model'][s+d:])
plt.figure(figsize=(17, 8))
plt.title('Mean Absolute Percentage Error: {0:.2f}%'.format(error))
plt.plot(forecast, color='r', label='model')
plt.axvspan(data.index[-1], forecast.index[-1],alpha=0.5, color='lightgrey')
plt.plot(data, label='actual')
plt.legend()
plt.grid(True)
plt.show()
def analyze(self):
data = self.read_data()
series = self.exponential_smoothing(data.Close, 0.05)
self.plot_exponential_smoothing(data.Close, [0.05, 0.15])
series = self.double_exponential_smoothing(data.Close, 0.02, 0.02)
self.plot_double_exponential_smoothing(data.Close, [.02, .9], [.02, .9])
lags = len(data.index) * 0.25
p_value = self.tsplot(data.Close, lags=int(lags))
if p_value:
data_diff = data.Close - data.Close.shift(1) # get rid of autocorrelation
self.tsplot(data_diff[1:], lags=int(lags))
# makes dickey-fuller p_value = 0, indicating no autocorrelation
ps = range(0, 3)
d = 1
qs = range(0, 3)
Ps = range(0, 3)
D = 1
Qs = range(0, 3)
s = 3
parameters = product(ps, qs, Ps, Qs)
parameters_list = list(parameters)
result_table = self.optimize_SARIMA(parameters_list, d, D, s, data)
p, q, P, Q = result_table.parameters[0]
best_model = sm.tsa.statespace.SARIMAX(data.Close, order=(p, d, q),
seasonal_order=(P, D, Q, s)).fit(disp=-1)
self.plot_SARIMA(data, best_model, 10)
print(best_model.predict(start=data.Close.shape[0], end=data.Close.shape[0] + 10))
print(self.mean_absolute_percentage_error(data.Close[s+d:], best_model.fittedvalues[s+d:]))
with open(f"./data_retriever_storage/timeseries/{TSA.ticker}.txt", 'w') as tracker:
tracker.write(str(data))
tracker.write('\n\nFrom the above data, our modelling predicted:\n')
tracker.write(str(best_model.predict(start=data.Close.shape[0], end=data.Close.shape[0] + 10)) + '\n')
tracker.write(str(TSA.mean_absolute_percentage_error(data.Close[s+d:], best_model.fittedvalues[s+d:])))
TSA = TimeSeriesAnalyzer('DOCU')
TSA.analyze()