When independence is assumed, forecasts of mortality for subpopulations are almost always divergent in the long term. We propose a method for coherent forecasting of mortality rates for two or more ...

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.

Chung Chen, Lon-Mu Liu, Joint Estimation of Model Parameters and Outlier Effects in Time Series, Journal of the American Statistical Association, Vol. 88, No. 421 (Mar., 1993), pp. 284-297 ...

We saw a wide range of company types, from very small mom-and-pop businesses to the Fortune 500 – proving that any organization can benefit from time-series forecasting.” ...

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 ...

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 ...

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 ...

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.