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Given the monthly gas prices from October 31, 2020, to September 30, 2024, the objective is to estimate the price on a specified date in the past and extrapolate it for one year into the future. The code will take a date as input and return a price estimate.
Strategy
Extrapolate Data Points
Utilize Linear Regression for Predictions
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Step 1: Understanding the Data
First, we need to convert the date format (MM/DD/YY) into a datetime format that can be utilized for analysis.
import pandas as pd
# Import data to a pandas DataFrame
df = pd.read_csv('input/Nat_Gas.csv')
df.head(5)The DataFrame should look like this:
Next, we convert these date strings into datetime objects.
from datetime import datetime
# Convert dates to datetime format
df['Dates'] = pd.to_datetime(df['Dates'])
# Set index as datetime format
df.set_index('Dates', inplace=True)
df.head(5)Output will be:
Step 2: Visualizing Historical Prices
To better understand the data, we can visualize the historical prices.
import matplotlib.pyplot as plt
# Plot the data
plt.figure(figsize=(12, 6))
plt.plot(df.index, df['Prices'], marker='o')
plt.title('Monthly Natural Gas Prices')
plt.xlabel('Date')
plt.ylabel('Price (in $)')
plt.grid()
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()
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Step 3: Predicting Future Prices
Now we apply linear regression to interpolate and extrapolate the data.
import numpy as np
from sklearn.linear_model import LinearRegression
def estimate_price(input_date):
input_date = pd.to_datetime(input_date)
# Extrapolate data for 12 months
future_dates = [input_date + timedelta(days=30 * i) for i in range(1, 13)]
# Prepare data for linear regression
df['Date_Ordinal'] = df.index.map(datetime.toordinal)
X = df['Date_Ordinal'].values.reshape(-1, 1)
y = df['Prices'].values
model = LinearRegression()
model.fit(X, y)
# Predict price for the input date
input_date_ordinal = np.array(input_date.toordinal()).reshape(-1, 1)
predicted_price = model.predict(input_date_ordinal)[0]
# Predict future prices
future_dates_ordinal = np.array([date.toordinal() for date in future_dates]).reshape(-1, 1)
future_prices = model.predict(future_dates_ordinal)
return predicted_price, future_dates, future_prices
input_date = "04/15/2023" # Change to the desired date
estimated_price, future_dates, future_prices = estimate_price(input_date)
print(f"Estimated price for {input_date}: ${estimated_price:.2f}")
for date, price in zip(future_dates, future_prices):
print(f"Estimated price for {date.date()}: ${price:.2f}")Example Output
Estimated price for 2023-04-15: $11.44
Estimated price for 2023-05-15: $11.48
Estimated price for 2023-06-14: $11.52
...
Estimated price for 2024-04-09: $11.90Step 4: Visualizing Predictions
Finally, let’s visualize both the historical data and the predictions.
plt.figure(figsize=(10, 6))
# Plot historical data
plt.plot(df.index, df['Prices'], label='Historical Prices', color='blue')
# Input date line
plt.axvline(pd.to_datetime(input_date), color='red', linestyle='--', label='Input Date')
# Future predictions
plt.plot(future_dates, future_prices, label='Future Predictions', color='orange', marker='o')
# Formatting the plot
plt.title('Price Estimation and Future Predictions')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
plt.grid()
plt.xticks(rotation=45)
plt.tight_layout()
# Show plot
plt.show()
This comprehensive approach provides a clear understanding of how to predict natural gas prices using linear regression, allowing for both historical analysis and future estimations.Given the monthly gas prices from October 31, 2020, to September 30, 2024, the objective is to estimate the price on a specified date in the past and extrapolate it for one year into the future. The code will take a date as input and return a price estimate.
Strategy
Extrapolate Data Points
Utilize Linear Regression for Predictions
Step 1: Understanding the Data
First, we need to convert the date format (MM/DD/YY) into a datetime format that can be utilized for analysis.
import pandas as pd
# Import data to a pandas DataFrame
df = pd.read_csv('input/Nat_Gas.csv')
df.head(5)The DataFrame should look like this:
Next, we convert these date strings into datetime objects.
from datetime import datetime
# Convert dates to datetime format
df['Dates'] = pd.to_datetime(df['Dates'])
# Set index as datetime format
df.set_index('Dates', inplace=True)
df.head(5)Output will be:
Step 2: Visualizing Historical Prices
To better understand the data, we can visualize the historical prices.
import matplotlib.pyplot as plt
# Plot the data
plt.figure(figsize=(12, 6))
plt.plot(df.index, df['Prices'], marker='o')
plt.title('Monthly Natural Gas Prices')
plt.xlabel('Date')
plt.ylabel('Price (in $)')
plt.grid()
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()
Step 3: Predicting Future Prices
Now we apply linear regression to interpolate and extrapolate the data.
import numpy as np
from sklearn.linear_model import LinearRegression
def estimate_price(input_date):
input_date = pd.to_datetime(input_date)
# Extrapolate data for 12 months
future_dates = [input_date + timedelta(days=30 * i) for i in range(1, 13)]
# Prepare data for linear regression
df['Date_Ordinal'] = df.index.map(datetime.toordinal)
X = df['Date_Ordinal'].values.reshape(-1, 1)
y = df['Prices'].values
model = LinearRegression()
model.fit(X, y)
# Predict price for the input date
input_date_ordinal = np.array(input_date.toordinal()).reshape(-1, 1)
predicted_price = model.predict(input_date_ordinal)[0]
# Predict future prices
future_dates_ordinal = np.array([date.toordinal() for date in future_dates]).reshape(-1, 1)
future_prices = model.predict(future_dates_ordinal)
return predicted_price, future_dates, future_prices
input_date = "04/15/2023" # Change to the desired date
estimated_price, future_dates, future_prices = estimate_price(input_date)
print(f"Estimated price for {input_date}: ${estimated_price:.2f}")
for date, price in zip(future_dates, future_prices):
print(f"Estimated price for {date.date()}: ${price:.2f}")Example Output
Estimated price for 2023-04-15: $11.44
Estimated price for 2023-05-15: $11.48
Estimated price for 2023-06-14: $11.52
...
Estimated price for 2024-04-09: $11.90Step 4: Visualizing Predictions
Finally, let’s visualize both the historical data and the predictions.
plt.figure(figsize=(10, 6))
# Plot historical data
plt.plot(df.index, df['Prices'], label='Historical Prices', color='blue')
# Input date line
plt.axvline(pd.to_datetime(input_date), color='red', linestyle='--', label='Input Date')
# Future predictions
plt.plot(future_dates, future_prices, label='Future Predictions', color='orange', marker='o')
# Formatting the plot
plt.title('Price Estimation and Future Predictions')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
plt.grid()
plt.xticks(rotation=45)
plt.tight_layout()
# Show plot
plt.show()
This comprehensive approach provides a clear understanding of how to predict natural gas prices using linear regression, allowing for both historical analysis and future estimations.