Forecasting_cash_flows_GAM

import pandas as pd
import numpy as np
from pygam import LinearGAM

# 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)

# Extract features and target variable
X = data.drop('cash_flow', axis=1)  # Adjust the column name according to your data
y = data['cash_flow']  # Adjust the column name according to your data

# Create a Generalized Additive Model (GAM)
model = LinearGAM().gridsearch(X.values, y.values)  # You can customize the grid search parameters if needed

# Once the model is trained, you can use it to forecast cash flows for future periods
future_periods = 12  # Adjust the number of future periods to forecast
future_dates = pd.date_range(start=data.index[-1], periods=future_periods + 1, closed='right')
future_data = pd.DataFrame(index=future_dates[1:], columns=X.columns)  # Use the last row as the starting point

# Forecast cash flows for future periods
for i in range(future_periods):
    future_data.iloc[i] = model.predict(future_data.iloc[i])

# Use the forecasted cash flows for further analysis or integration into your ERP accounting system