Forecasting_cash_flows_ARIMA

import pandas as pd
import numpy as np
from statsmodels.tsa.arima.model import ARIMA

# Load cash flow data into a pandas DataFrame
data = pd.read_csv('cash_flow_data.csv')

# Preprocess the data if needed (e.g., handle missing values, adjust data types)

# Convert the date column to a datetime type
data['date'] = pd.to_datetime(data['date'])

# Set the date column as the index
data.set_index('date', inplace=True)

# Create a train-test split (adjust the ratio based on your data)
train_size = int(len(data) * 0.8)
train_data, test_data = data.iloc[:train_size], data.iloc[train_size:]

# Fit the ARIMA model to the training data
model = ARIMA(train_data, order=(1, 1, 1))  # Adjust the order parameter based on your data
model_fit = model.fit()

# Forecast cash flows for the test period
forecasted_values = model_fit.forecast(steps=len(test_data))[0]

# Evaluate the forecast performance (e.g., calculate metrics like mean squared error, MAPE)

# Visualize the forecasted values and compare them with the actual values
forecasted_df = pd.DataFrame(forecasted_values, index=test_data.index, columns=['forecast'])
comparison_df = pd.concat([test_data, forecasted_df], axis=1)

# Once the model is trained and evaluated, you can use it to forecast cash flows for future periods
future_periods = 12  # Adjust the number of future periods to forecast
future_forecast = model_fit.forecast(steps=future_periods)[0]

# Use the future_forecast for further analysis or integration into your ERP accounting system