This code loads the sales data from a CSV file, sets the date as the index, and aggregates the sales by day. It then splits the data into training and testing sets, fits a SARIMA model to the training data, and forecasts the sales for the next 30 days. Finally, it plots the actual and forecasted sales, along with the confidence intervals. Note that you may need to adjust the model parameters (order and seasonal_order) depending on your data. import pandas as pd import numpy as np import matplotlib.pyplot as plt from statsmodels.tsa.statespace.sarimax import SARIMAX # Load sales data sales_data = pd.read_csv('sales_data.csv', parse_dates=['date']) # Set date as index and aggregate sales by day sales_data.set_index('date', inplace=True) sales_data = sales_data.resample('D').sum() # Split data into training and testing sets train_data = sales_data.iloc[:-30] test_data = sales_data.iloc[-30:] # Fit SARIMA model to training data model = SARIMAX(train_data, o...
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