Optimize Supply Chain Forecasts with Darts and Amazon Forecast SDK

                      
                     
import os
import pandas as pd
from darts import TimeSeries
from darts.models import NBEATSModel
from darts.utils import timeseries
import boto3

# Configuration
AWS_REGION = os.getenv('AWS_REGION', 'us-east-1')
FORECAST_SERVICE = boto3.client('forecast', region_name=AWS_REGION)
FORECAST_NAME = os.getenv('FORECAST_NAME')

# Function to load and prepare data
def load_data(file_path: str) -> TimeSeries:
    try:
        df = pd.read_csv(file_path)
        series = TimeSeries.from_dataframe(df, time_col='date', value_cols='value')
        return series
    except Exception as e:
        print(f"Error loading data: {e}")

# Function to create and train model
def train_model(series: TimeSeries) -> NBEATSModel:
    model = NBEATSModel(input_chunk_length=24, output_chunk_length=12)
    model.fit(series)
    return model

# Function to make predictions
def make_forecast(model: NBEATSModel, n: int) -> TimeSeries:
    future = model.predict(n)
    return future

# Main execution
if __name__ == '__main__':
    try:
        data_series = load_data('path/to/your/data.csv')  # Update with your data path
        model = train_model(data_series)
        forecast = make_forecast(model, 12)
        print(f"Forecasted values: {forecast.values()}")
    except Exception as e:
        print(f"An error occurred: {e}")