Finance and Accounting Automation - Financial Forecasting Model

 Notes:

• What problem does it solve?: Automates financial forecasting based on historical data.

• How can businesses benefit from customizing the code?: Businesses can adjust for seasonal trends and other factors to get more accurate predictions.

• How can businesses adopt the solution further?: Integrate with external data sources to adjust forecasts in real time.

Actual Python Code:

import pandas as pd

import numpy as np

from statsmodels.tsa.holtwinters import ExponentialSmoothing

class FinancialForecasting:

    def __init__(self, historical_data):

        self.historical_data = historical_data

    def forecast(self, forecast_period=12):

        model = ExponentialSmoothing(self.historical_data, trend='add', seasonal='add', seasonal_periods=12)

        model_fit = model.fit()

        forecast = model_fit.forecast(forecast_period)

        return forecast

    def display_forecast(self):

        forecast = self.forecast()

        print(f"Forecasted Values for the Next Periods:")

        for i, value in enumerate(forecast):

            print(f"Month {i+1}: ${value:.2f}")

# Example usage

historical_data = [12000, 12500, 13000, 13500, 14000, 14500, 15000, 15500, 16000, 16500, 17000, 17500]

forecasting = FinancialForecasting(historical_data)

forecasting.display_forecast()