Example input
[describe your dataset or time series] [describe the variable or aspect you want to forecast, e.g., stock prices, sales volumes, weather patterns, etc.]
Example output
Output Example:
For the next twelve months, here are the predicted monthly sales volumes for the e-commerce platform along with corresponding 95% confidence intervals:
January 2024: Predicted Sales Volume - 150,000 units (CI: 145,000 - 155,000)
February 2024: Predicted Sales Volume - 155,500 units (CI: 152,000 - 159,000)
March 2024: Predicted Sales Volume - 148,200 units (CI: 144,500 - 152,000)
April 2024: Predicted Sales Volume - 160,800 units (CI: 157,000 - 164,500)
May 2024: Predicted Sales Volume - 165,300 units (CI: 161,500 - 169,000)
June 2024: Predicted Sales Volume - 170,000 units (CI: 166,000 - 174,000)
July 2024: Predicted Sales Volume - 172,500 units (CI: 169,000 - 176,000)
August 2024: Predicted Sales Volume - 168,900 units (CI: 165,500 - 172,300)
September 2024: Predicted Sales Volume - 165,700 units (CI: 162,000 - 169,500)
October 2024: Predicted Sales Volume - 160,200 units (CI: 156,500 - 163,800)
November 2024: Predicted Sales Volume - 155,800 units (CI: 152,000 - 159,500)
December 2024: Predicted Sales Volume - 175,000 units (CI: 171,000 - 179,000)
These predictions were generated using a combined ARIMA and LSTM model, taking into account past sales data, seasonal effects, promotional campaigns, and economic indicators. The confidence intervals indicate the range within which the actual sales volumes are expected to fall with 95% certainty. The upward trend in sales during the holiday season is evident from the forecasted values, highlighting the importance of promotional campaigns during this period.