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Please implement in Python the evolution of two multi-attribute parameters with different cardinalities and a common variable core while the rest of their variables differ. Suppose I have a large dataset and complex relationships, so I should employ machi

Wrong Whale answered on October 21, 2023 Popularity 1/10 Helpfulness 1/10

answer Please implement in Python the evolution of two multi-attribute parameters with different cardinalities and a common variable core while the rest of their variables differ. Suppose I have a large dataset and complex relationships, so I should employ machi

Please implement in Python the evolution of two multi-attribute parameters with different cardinalities and a common variable core while the rest of their variables differ. Suppose I have a large dataset and complex relationships, so I should employ machi

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import pandas as pd
import numpy as np
from statsmodels.tsa.arima_model import ARIMA
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt

# Generate sample time series data for two parameters
np.random.seed(0)
n = 100
core_data = np.cumsum(np.random.normal(0, 1, n))
param1_data = core_data + np.random.normal(0, 1, n)
param2_data = core_data + np.random.normal(0, 1, n)

# Create dataframes
df = pd.DataFrame({'Core': core_data, 'Parameter1': param1_data, 'Parameter2': param2_data})

# Split the data into training and testing
train_size = int(0.8 * len(df))
train, test = df[:train_size], df[train_size:]

# Define a function to evaluate ARIMA model
def evaluate_arima_model(train, test, order):
    model = ARIMA(train, order=order)
    model_fit = model.fit(disp=0)
    forecast = model_fit.forecast(steps=len(test))
    mse = mean_squared_error(test, forecast)
    return mse

# Define a function to find the best ARIMA model order
def find_best_arima_order(train, p_values, d_values, q_values):
    best_mse, best_order = float('inf'), None
    for p in p_values:
        for d in d_values:
            for q in q_values:
                order = (p, d, q)
                try:
                    mse = evaluate_arima_model(train, test, order)
                    if mse < best_mse:
                        best_mse, best_order = mse, order
                except:
                    continue
    return best_order

# Set the range of ARIMA parameters
p_values = [0, 1, 2]
d_values = [0, 1, 2]
q_values = [0, 1, 2]

# Find the best ARIMA order for both parameters
best_order_param1 = find_best_arima_order(train['Parameter1'], p_values, d_values, q_values)
best_order_param2 = find_best_arima_order(train['Parameter2'], p_values, d_values, q_values)

# Fit ARIMA models with the best orders
model_param1 = ARIMA(train['Parameter1'], order=best_order_param1)
model_param1_fit = model_param1.fit(disp=0)
forecast_param1 = model_param1_fit.forecast(steps=len(test))

model_param2 = ARIMA(train['Parameter2'], order=best_order_param2)
model_param2_fit = model_param2.fit(disp=0)
forecast_param2 = model_param2_fit.forecast(steps=len(test))

# Plot the results
plt.plot(test['Parameter1'], label='Actual Parameter1')
plt.plot(forecast_param1, color='red', label='Predicted Parameter1')
plt.plot(test['Parameter2'], label='Actual Parameter2')
plt.plot(forecast_param2, color='green', label='Predicted Parameter2')
plt.legend()
plt.show()

Popularity 1/10 Helpfulness 1/10 Language python

Source: chat.openai.com

Tags: core parameters python rest variables

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Contributed on Oct 21 2023

Wrong Whale

0 Answers Avg Quality 2/10

Please implement in Python the evolution of two multi-attribute parameters with different cardinalities and a common variable core while the rest of their variables differ. Suppose I have a large dataset and complex relationships, so I should employ machi

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