Pre-trained foundation models are making time-series forecasting more accessible and available, unlocking its benefits for smaller organizations with limited resources.

We explore the added value of deep learning techniques for forecasting and nowcasting in official statistics as an alternative to classic time series models. Several neural network algorithms are ...

We’ll also use the InfluxDB Python client library to query data from InfluxDB and convert the data to a Pandas DataFrame to make working with the time series data easier.

The 2023 paper “Time Series-Based Quantitative Risk Models: Enhancing Accuracy in Forecasting and Risk Assessment” by Olanrewaju Olukoya Odumuwagun, published in the International Journal of ...

Attention is not all you need when forecasting with generative AI. You also need time. IBM recently made its open-source TinyTimeMixer model available on Hugging Face.

Use automated methods to estimate the best fit model parameters. Apply the Augmented Dickey-Fuller method (ADF) to statistically test a time series. Estimate the number of parameters for a SARIMA ...

LinkedIn today open-sourced Greykite, a Python library for long- and short-term predictive analytics. Greykite’s main algorithm, Silverkite, delivers automated forecasting, which LinkedIn says ...

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 ...