Discover the Thrills of Football Cup Armenia
Football Cup Armenia is the pinnacle of excitement and competition in the Armenian football scene. With matches updated daily, enthusiasts and bettors alike are kept on the edge of their seats. Our platform provides expert betting predictions, ensuring you have the insights needed to make informed decisions. Dive into the world of Armenian football where passion meets strategy, and every match tells a story.
Why Football Cup Armenia?
- Rich History: The tournament has a storied past, showcasing the best talents from across Armenia. Each match is a chapter in this ongoing saga.
- Daily Updates: Stay current with live updates and scores. Our platform ensures you never miss a moment of the action.
- Expert Predictions: Rely on our seasoned analysts who provide betting predictions based on in-depth analysis and trends.
- Community Engagement: Join a community of passionate fans and experts who share insights, discuss matches, and celebrate victories together.
How to Navigate Our Platform
Navigating our platform is seamless and user-friendly. Here’s how you can make the most out of it:
- Live Scores: Access real-time scores for all matches. Whether you’re at home or on the go, stay connected with live updates.
- Detailed Match Analysis: Gain insights into each match with comprehensive analysis. Understand team strategies, player form, and historical performances.
- Betting Tips: Our experts provide daily betting tips. From underdog picks to surefire bets, get predictions tailored to enhance your betting experience.
- User Forums: Engage with other users in our forums. Share your thoughts, ask questions, and get advice from fellow football enthusiasts.
The Teams to Watch
The Football Cup Armenia features some of the most competitive teams in the region. Here’s a closer look at the teams that are making waves this season:
- Pyunik Yerevan: Known for their strategic gameplay and strong defense, Pyunik Yerevan is a team that consistently performs well under pressure.
- Alashkert FC: With a dynamic offense and skilled midfielders, Alashkert FC is always a formidable opponent on the field.
- Noah Yerevan: A rising star in Armenian football, Noah Yerevan brings youthful energy and innovative tactics to their matches.
- Banants Erevan: With a rich history and experienced players, Banants Erevan continues to be a strong contender in every tournament.
Expert Betting Predictions
Betting on football can be both thrilling and rewarding if done wisely. Our platform offers expert betting predictions that are based on thorough analysis of team form, player statistics, and historical data. Here’s how our predictions can benefit you:
- Data-Driven Insights: Our analysts use advanced algorithms and data analytics to provide accurate predictions.
- Trend Analysis: Stay ahead of the curve by understanding betting trends and market movements.
- Daily Updates: Receive daily updates on predictions to keep your betting strategy fresh and relevant.
- User-Friendly Interface: Access all predictions through an intuitive interface that makes it easy to follow your favorite matches and bets.
In-Depth Match Analysis
Each match in the Football Cup Armenia is more than just a game; it’s an opportunity to witness strategy, skill, and sportsmanship at its finest. Here’s what you can expect from our in-depth match analysis:
- Tactical Breakdowns: Understand the tactical nuances of each team’s approach to the game.
- Player Performances: Get insights into key player performances and how they might impact the outcome of the match.
- Historical Context: Learn about past encounters between teams and how they might influence future matches.
- Prediction Models: Explore our prediction models that combine statistical analysis with expert opinions to forecast match results.
The Role of Community in Football Cup Armenia
The community aspect of Football Cup Armenia is vital for its success. Fans not only support their teams but also contribute to a vibrant ecosystem of discussion and engagement. Here’s how you can be part of this community:
- Social Media Engagement: Follow us on social media platforms for live discussions, updates, and fan interactions.
- User Forums: Participate in our forums where fans share their insights, predictions, and experiences.
- Venue Events: Attend live matches and events to experience the thrill of Armenian football firsthand.
- Crowdsourcing Predictions: Contribute your own predictions and see how they compare with those of our experts.
Frequently Asked Questions (FAQs)
<|file_sep|># -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
from datetime import datetime
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
from math import sqrt
import statsmodels.api as sm
from statsmodels.tsa.arima_model import ARIMA
from statsmodels.tsa.stattools import adfuller
import matplotlib.pyplot as plt
def plot_series(series):
fig = plt.figure(figsize=(10,6))
plt.plot(series.values)
plt.grid(True)
plt.show()
def plot_series_xy(x,y):
fig = plt.figure(figsize=(10,6))
plt.plot(x,y)
plt.grid(True)
plt.show()
def train_test_split(df,n_test):
return df[:-n_test], df[-n_test:]
def scale(train,test):
# train = train.astype('float64')
# test = test.astype('float64')
# scaler = MinMaxScaler(feature_range=(-1,1))
# scaler = scaler.fit(train)
# # Apply transform
# train = train.reshape(-1,1)
# test = test.reshape(-1,1)
train_scaled = (train - train.mean()) / (train.max() - train.min())
test_scaled = (test - train.mean()) / (train.max() - train.min())
return scaler, train_scaled , test_scaled
def invert_scale(scaler,yhat,x):
new_row = [x for x in yhat]
new_row.append(y)
inverted = scaler.inverse_transform(new_row)
return inverted[0]
def forecast(train,test,model):
predictions = list()
for i in range(len(test)):
model_fit = model.fit(train[:i+1])
yhat = model_fit.forecast()[0]
predictions.append(yhat)
return predictions
def rmse(predictions,test):
return sqrt(mean_squared_error(test,predictions))
def mape(predictions,test):
return np.mean(np.abs((test-predictions)/test))*100
def evaluate_forecast(test,predictions):
errors = abs(predictions-test)
mape= np.mean(errors/test)*100
print('MAPE: %.3f%%' % mape)
def fit_ARIMA_model(train,test,p,d,q):
model = ARIMA(train,(p,d,q))
model_fit=model.fit(disp=0)
output=model_fit.forecast(steps=len(test))
predictions=output[0]
rmse=rmse(predictions,test)
print('Test RMSE: %.3f' % rmse)
return model_fit,predictions
def adfuller_test(series,title=''):
result=adfuller(series.dropna(),autolag='AIC')
labels = ['ADF test statistic','p-value','# lags used','# observations']
out = pd.Series(result[0:4],index=labels)
for key,val in result[4].items():
out['critical value (%s)'%key] = val
print(title)
print(out.to_string())
if result[1] <= 0.05:
print("Strong evidence against the null hypothesis")
print("Reject the null hypothesis")
print("Data has no unit root and is stationary")
else:
print("Weak evidence against null hypothesis")
print("Fail to reject the null hypothesis")
print("Data has a unit root, indicating it is non-stationary ")
def seasonal_decompose(df,column_name,freq):
df=df[column_name]
result=sm.tsa.seasonal_decompose(df,model='additive',freq=freq)
result.plot()
plt.show()
if __name__ == '__main__':
df=pd.read_csv('data/airline_passengers.csv',index_col='Month',parse_dates=True)
df=df.dropna()
x=df.index.values
y=df['Passengers'].values
x_train,x_test=train_test_split(x,len(x)-12*3)
y_train,y_test=train_test_split(y,len(y)-12*3)
scaler,_ ,_ =scale(y_train,y_test)
model_fit,predictions=fit_ARIMA_model(y_train,y_test,p=4,d=1,q=0)
predictions_invert=np.array([invert_scale(scaler,x) for x in predictions])
evaluate_forecast(y_test,predictions_invert)
y_hat_avg=y_train.copy()
y_hat_avg['moving_avg_forecast']=y_train['Passengers'].rolling(30).mean()
y_hat_avg['moving_avg_forecast'][len(y_hat_avg)-len(y_test):].plot(color='red',legend=True)
y_test.plot()
plt.legend(loc='best')
plt.title('Moving Average Forecast')
plt.show()
df['forecast']=np.nan
df.loc[len(df)-len(y_test):,'forecast']=predictions_invert
df[['Passengers','forecast']].plot(figsize=(12,8))
plt.legend(loc='best')
plt.title('RMSE: %.4f'%rmse(predictions_invert,y_test))
plt.show()
adfuller_test(df['Passengers'])
adfuller_test(df['Passengers'].diff().dropna())
freq=int(len(df)/12)
seasonal_decompose(df,'Passengers',freq)<|repo_name|>gerardosubtil/TimeSeries<|file_sep|>/ARIMA_Examples.py
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
from datetime import datetime
import matplotlib.pyplot as plt
from statsmodels.tsa.arima_model import ARIMA
from sklearn.metrics import mean_squared_error
import warnings
warnings.filterwarnings('ignore')
plt.style.use('fivethirtyeight')
df=pd.read_csv('data/AirPassengers.csv',index_col='Month',parse_dates=True)
x=df.index.values
y=df['#Passengers'].values
x_train,x_test=train_test_split(x,len(x)-12*3)
y_train,y_test=train_test_split(y,len(y)-12*3)
model_fit,predictions=fit_ARIMA_model(y_train,y_test,p=4,d=1,q=0)
evaluate_forecast(y_test,predictions)
df['forecast']=np.nan
df.loc[len(df)-len(y_test):,'forecast']=predictions_invert
df[['Passengers','forecast']].plot(figsize=(12,8))
plt.legend(loc='best')
plt.title('RMSE: %.4f'%rmse(predictions_invert,y_test))
plt.show()
df=pd.read_csv('data/AirlinePassengers.csv',index_col='Month',parse_dates=True)
df=df.dropna()
x=df.index.values
y=df['Passengers'].values
x_train,x_test=train_test_split(x,len(x)-12*3)
y_train,y_test=train_test_split(y,len(y)-12*3)
model_fit,predictions=fit_ARIMA_model(y_train,y_test,p=4,d=1,q=0)
predictions_invert=np.array([invert_scale(scaler,x) for x in predictions])
evaluate_forecast(y_test,predictions_invert)
y_hat_avg=y_train.copy()
y_hat_avg['moving_avg_forecast']=y_train['Passengers'].rolling(30).mean()
y_hat_avg['moving_avg_forecast'][len(y_hat_avg)-len(y_test):].plot(color='red',legend=True)
y_test.plot()
plt.legend(loc='best')
plt.title('Moving Average Forecast')
plt.show()
df['forecast']=np.nan
df.loc[len(df)-len(y_test):,'forecast']=predictions_invert
df[['Passengers','forecast']].plot(figsize=(12,8))
plt.legend(loc='best')
plt.title('RMSE: %.4f'%rmse(predictions_invert,y_test))
plt.show()
def predict_next_month(train,test,model_fit):
yhat=model_fit.forecast()[0]
return yhat[0]
model_fit=predict_next_month(train,test,model_fit)
test=np.append(train,test,axis=None)[-len(test):]
model_fit.forecast(steps=len(test))[0]<|repo_name|>gerardosubtil/TimeSeries<|file_sep|>/README.md
# Time Series Project
## Description
In this project I'm going to build some models based on time series.
## Getting Started
These instructions will get you a copy of the project up and running on your local machine for development purposes.
### Prerequisites
You need to install Python libraries:
pip install pandas
pip install numpy
pip install matplotlib
pip install scikit-learn
pip install statsmodels
### Installing
Clone repository:
git clone https://github.com/gerardosubtil/TimeSeries.git
### Running
Run script:
python3 TimeSeries.py
## Authors
* **Gerardo Subtil** - *Initial work* - [gerardosubtil](https://github.com/gerardosubtil)
## License
This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details<|repo_name|>gerardosubtil/TimeSeries<|file_sep|>/TimeSeries.py
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
from datetime import datetime
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from math import sqrt
import statsmodels.api as sm
from statsmodels.tsa.arima_model import ARIMA
from statsmodels.tsa.stattools import adfuller
warnings.filterwarnings('ignore')
plt.style.use('fivethirtyeight')
class TimeSeries:
def __init__(self,data_path,column_name):
self.data_path=data_path
self.column_name=column_name
def load_data(self):
self.df=pd.read_csv(self.data_path,index_col=self.column_name,
parse_dates=True,
header=0)
def drop_na(self):
self.df=self.df.dropna()
def plot_series(self):
self.df[self.column_name].plot(figsize=(10,6))
def plot_series_xy(self,x,y):
plt.plot(x,y)
def train_and_plot(self,n):
self.train,self.test=self.split(n=n)
def split(self,n):
return self.df[:-n], self.df[-n:]
def scale(self):
self.scaler,self.train_scaled,self.test_scaled=self.scale_func(self.train,self.test)
def scale_func(self,X_train,X_val):
X_train=X_train.astype('float64')
X_val=X_val.astype('float64')
scaler = MinMaxScaler(feature_range=(-1,1))
scaler=scaler.fit(X_train)
# Apply transform
X_train=scaler.transform(X_train.reshape(-1,1))
X_val=scaler.transform(X_val.reshape(-1,1))
return scaler,X_train,X_val
def invert_scale(self,scaler,Yhat,X):
new_row=[X for X in Yhat]
new_row.append(Y)
inverted=scaler.inverse_transform(new_row)
return inverted[0]
def forecast(self,model):
self.predictions=list()
for i in range(len(self.test)):
model_fit=model.fit(self.train[:i+1])
yhat=model_fit.forecast()[0]
self.predictions.append(yhat)
return self.predictions
def rmse(self):
return sqrt(mean_squared_error(self.test,self.predictions))
def mape(self):
return np.mean(np.abs((self.test-self.predictions)/self.test))*100
def evaluate_forecast(self):
errors=np.abs(self.predictions-self.test)
mape=np.mean(errors/self.test)*100
print('MAPE: %.3f%%' % mape)
def fit_ARIMA_model(self,p,d,q):
model=ARIMA(self.train,(p,d,q))
model_fit=model.fit(disp=0)
output=model_fit.forecast(steps=len(self.test))
self.predictions=output[0]
self.rmse=self.rmse()
print('Test RMSE: %.3f' % self.rmse)
return model_fit,self.predictions
def adfuller_test(self,series,title=''):
result=adfuller(series.dropna(),autolag='AIC')
labels=['ADF test statistic','p-value','# lags used','# observations']
out=pd.Series(result[0:4],index=labels)
for key,val in result[4].items():
out['critical value (%s)'%key]=val
print(title)
print(out.to_string())
if result[1]<=0.05:
print("Strong evidence against the null hypothesis")
print("Reject the null hypothesis")
print("Data has no unit root and is stationary")
else:
print("Weak evidence against null hypothesis")
print("Fail to reject the null hypothesis")
print("Data has a unit root, indicating it is non-stationary ")
def seasonal_decompose(self,freq):
result=sm.tsa.seasonal_decompose(df[model],model='additive',freq=freq)
result.plot()
plt.show()
if __name__ == '__main__':
ts=TimeSeries(data_path='data/AirlinePassengers.csv',
column_name='
