Sales and demand forecasting has evolved markedly with the convergence of traditional statistical techniques and cutting‐edge machine learning methods. Time series analysis remains central to ...
Singular Spectrum Analysis (SSA) is a powerful nonparametric method that has emerged as a vital tool in the analysis and forecasting of time series data. By utilising matrix decomposition techniques, ...
Artificial intelligence (AI) technologies are currently revolutionizing industries and enabling automation on a scale we've never seen before. Of course, none of this is possible without data. These ...
Discover how Fourier Analysis breaks down complex time series data into simpler components to identify trends and patterns, despite its limitations in stock forecasting.
Recent advances in AI, such as foundation models, make it possible for smaller companies to build custom models to make predictions, reduce uncertainty, and gain business advantage. Time series ...
In the wake of the disruptive debut of DeepSeek-R1, reasoning models have been all the rage so far in 2025. IBM is now joining the party, with the debut today of its Granite 3.2 large language model ...
According to IBM, attention is not all you need when forecasting certain outcomes with generative AI. You also need time. Earlier this year, IBM made its open-source TinyTimeMixer (TTM) model ...
Legacy load forecasting models are struggling with ever-more-common, unpredictable events; power-hungry AI offers a solution.
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