1 day ago · Supply chain predictive analytics harnesses historical data, statistical algorithms, and machine learning to foresee future trends and occurrences.

5 days ago · Time-series forecasting of seasonal items sales using machine learning – A comparative analysis. International Journal of Information Management Data Insights ...

5 days ago · To model supply chains, we can represent them as temporal (or dynamic) graphs, where nodes are firms and edges represent time-varying transactions between ...

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2 days ago · The study [30] explores using data from a pool of time series to train a generalized regression neural network for individual series forecasting, showing ...

9 hours ago · The most commonly used time series analysis methods for forecasting in project management are LSTM (Long Short Term Memory) and ARIMA (Autoregressive Integrated ...

13 hours ago · Time-series analysis is used to detect patterns in a time series. The most important patterns are Trend, Seasonality, Continuous, Irregular, and ...

7 days ago · Utilizing either quantitative methodologies such as time series analysis and regression, or qualitative approaches like consumer surveys and managerial ...

6 days ago · Time series forecasting is a fundamental task with a wide range of applications, from supply chain management to weather forecasting and stock market analysis.