The granulation effect will further affect the forecasting performance of the time series. To this end, this paper establishes a convolutional neural network (CNN) prediction method based on improved ...

AI, particularly machine learning techniques, excels at tackling P-class problems. Examples include time series forecasting tasks like retail demand prediction and point-of-sale volume forecasting ...

However, most state-of-the-art methods suffer from limitations due to not only ... To address these problems, this paper proposes a multi-scale information granule-based time series forecasting model.

El-Dosuky, M. , Alowaydan, R. and Alqarni, B. (2024) Wind Power Forecasting Using Grey Wolf Optimized Long Short-Term Memory ...

Forecasting capital expenditures (capex) is a crucial step in discounted cash flow (DCF) analysis, as it affects the free cash flow (FCF) and the terminal value of a company. Capex represents the ...

All the modules have a load method which you can use to load the dataset for a specific group. If you don’t have the data locally it will be downloaded for you.

So we created a library that can be used to forecast in production environments or as benchmarks. StatsForecast includes an extensive battery of models that can efficiently fit millions of time series ...