Predicting Future Interest Rates
In this project, we demonstrate a practical approach to forecasting interest rates using a Linear Regression model in Python. The goal is to analyze historical interest rate data, build a predictive model, and generate forecasts for future periods.
Financial analysts can use these techniques to forecast interest rates, aiding in portfolio
management and strategic decision-making.
Economists and policymakers can utilize these forecasts to assess economic conditions and formulate
effective monetary policies.
Load and Prepare the Data
pd.read_csv(): This function reads the CSV file into a pandas DataFrame (data). The
parse_dates=['Date'] argument ensures that the 'Date' column is parsed as dates. dayfirst=True
handles dates in DD/MM/YYYY format appropriately, and index_col='Date' sets the 'Date' column as the
index of the DataFrame.
file_path = 'interest_rates.csv'
data = pd.read_csv(file_path, parse_dates=['Date'], dayfirst=True, index_col='Date')
Plotting Historical Data
plt.figure(figsize=(10, 6)): Creates a new figure for plotting with a size of 10 by 6.
plt.plot(): Plots the historical interest rates (data['Interest_Rate']) against dates (data.index).
label='Historical Data': Assigns a label to this plot for the legend.
plt.figure(figsize=(10, 6))
plt.plot(data.index, data['Interest_Rate'], label='Historical Data')
Forecasting using Linear Regression Model
data['Days'] = (data.index - data.index[0]).days: Calculates the number of days from the first date
in the dataset (data.index[0]). This helps in creating a simple time-based predictor for the Linear
Regression model.
X = data[['Days']]: Selects the 'Days' column as the predictor variable (X).
y = data['Interest_Rate']: Selects the 'Interest_Rate' column as the target variable (y).
model = LinearRegression(): Initializes a Linear Regression model from scikit-learn.
model.fit(X, y): Fits the model using the predictors (X) and the target (y), training the model to
predict interest rates based on the number of days.
data['Days'] = (data.index - data.index[0]).days
X = data[['Days']]
y = data['Interest_Rate']
model = LinearRegression()
model.fit(X, y)
Generating and Plotting Forecasts
future_steps = 180: Specifies the number of days to forecast.
future_index = pd.date_range(start=data.index[-1] + pd.Timedelta(days=1), periods=future_steps,
freq='D'): Generates a new date range (future_index) starting from the day after the last date in
data, spanning future_steps days with a frequency of daily ('D').
future_days = (future_index - data.index[0]).days: Calculates the number of days from the first date
in the dataset for the future dates.
future_days.values.reshape(-1, 1): Reshapes future_days into a 2D array for prediction by the Linear
Regression model.
forecast = model.predict(future_days): Predicts future interest rates (forecast) using the trained
Linear Regression model.
forecast_data = pd.DataFrame(forecast, index=future_index, columns=['Forecasted Interest Rate']):
Creates a DataFrame (forecast_data) to store the forecasted interest rates with dates as index and a
column name for clarity.
plt.plot(forecast_data.index, forecast_data['Forecasted Interest Rate'], label='Forecast',
linestyle='--', color='orange'): Plots the forecasted interest rates against dates
(forecast_data.index).
future_steps = 180
future_index = pd.date_range(start=data.index[-1] + pd.Timedelta(days=1), periods=future_steps, freq='D')
future_days = (future_index - data.index[0]).days
future_days = future_days.values.reshape(-1, 1)
forecast = model.predict(future_days)
forecast_data = pd.DataFrame(forecast, index=future_index, columns=['Forecasted Interest Rate'])
plt.plot(forecast_data.index, forecast_data['Forecasted Interest Rate'], label='Forecast', linestyle='--', color='orange')
Finalizing the Plot
plt.title('Interest Rate Forecast for the Next 2 Months'): Sets the title of the plot.
plt.xlabel('Date'): Labels the x-axis as 'Date'.
plt.ylabel('Interest Rate'): Labels the y-axis as 'Interest Rate'.
plt.legend(): Displays the legend to distinguish between 'Historical Data' and 'Forecast'.
plt.tight_layout(): Adjusts subplot parameters to give a cleaner appearance.
plt.show(): Displays the plot.
plt.title('Interest Rate Forecast for the Next Months')
plt.xlabel('Date')
plt.ylabel('Interest Rate')
plt.legend()
plt.tight_layout()
plt.show()
Below is the full code with additional comments embedded.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
# Load the dataset
file_path = 'interest_rates.csv'
# Read the data
data = pd.read_csv(file_path, parse_dates=['Date'], dayfirst=True, index_col='Date')
# Ensure all values are numeric and handle non-numeric values
data['Interest_Rate'] = pd.to_numeric(data['Interest_Rate'], errors='coerce')
# Drop rows with any NaN values
data.dropna(inplace=True)
# Ensure the data is sorted by date
data.sort_index(inplace=True)
# Plot the historical data
plt.figure(figsize=(10, 6))
plt.plot(data.index, data['Interest_Rate'], label='Historical Data')
# Prepare the data for forecasting
data['Days'] = (data.index - data.index[0]).days
X = data[['Days']]
y = data['Interest_Rate']
# Fit a linear regression model
model = LinearRegression()
model.fit(X, y)
# Forecast future interest rates
future_steps = 180 # Number of days to forecast
future_index = pd.date_range(start=data.index[-1] + pd.Timedelta(days=1), periods=future_steps, freq='D')
future_days = (future_index - data.index[0]).days
future_days = future_days.values.reshape(-1, 1)
forecast = model.predict(future_days)
# Create a DataFrame for the forecasted data
forecast_data = pd.DataFrame(forecast, index=future_index, columns=['Forecasted Interest Rate'])
# Plot the forecasted data
plt.plot(forecast_data.index, forecast_data['Forecasted Interest Rate'], label='Forecast', linestyle='--', color='orange')
# Plot labels and legend
plt.title('Interest Rate Forecast for the Next Months')
plt.xlabel('Date')
plt.ylabel('Interest Rate')
plt.legend()
plt.tight_layout()
plt.show()
