import numpy as np
from statsmodels.tsa.arima.model import ARIMA
import cupy as cp

class TimeSeriesModels:
    def __init__(self, data):
        self.data = np.array(data)
    
    def kalman_filter(self, observations, transition_matrix, observation_matrix, 
                     process_noise, measurement_noise):
        n = len(observations)
        state_mean = np.zeros(n)
        state_cov = np.zeros(n)
        
        for t in range(1, n):
            # Predict
            pred_mean = transition_matrix * state_mean[t-1]
            pred_cov = transition_matrix * state_cov[t-1] * transition_matrix + process_noise
            
            # Update
            kalman_gain = pred_cov * observation_matrix / (observation_matrix * pred_cov * observation_matrix + measurement_noise)
            state_mean[t] = pred_mean + kalman_gain * (observations[t] - observation_matrix * pred_mean)
            state_cov[t] = (1 - kalman_gain * observation_matrix) * pred_cov
            
        return state_mean, state_cov
    
    def garch(self, returns, p=1, q=1):
        T = len(returns)
        omega = np.var(returns) * 0.1
        alpha = 0.1
        beta = 0.8
        
        sigma2 = np.zeros(T)
        sigma2[0] = np.var(returns)
        
        for t in range(1, T):
            sigma2[t] = omega + alpha * returns[t-1]**2 + beta * sigma2[t-1]
            
        return np.sqrt(sigma2)