Finite-Interval Forecasting Engine: Machine learning models for discrete-time survival analysis and finite time series forecasting
Project description
The Finite-Interval Forecasting Engine (FIFE) provides machine learning and other models for discrete-time survival analysis and finite time series forecasting.
Suppose you have a dataset that looks like this:
| ID | period | feature_1 | feature_2 | feature_3 | ... |
|---|---|---|---|---|---|
| 0 | 2016 | 7.2 | A | 2AX | ... |
| 0 | 2017 | 6.4 | A | 2AX | ... |
| 0 | 2018 | 6.6 | A | 1FX | ... |
| 0 | 2019 | 7.1 | A | 1FX | ... |
| 1 | 2016 | 5.3 | B | 1RM | ... |
| 1 | 2017 | 5.4 | B | 1RM | ... |
| 2 | 2017 | 6.7 | A | 1FX | ... |
| 2 | 2018 | 6.9 | A | 1RM | ... |
| 2 | 2019 | 6.9 | A | 1FX | ... |
| 3 | 2017 | 4.3 | B | 2AX | ... |
| 3 | 2018 | 4.1 | B | 2AX | ... |
| 4 | 2019 | 7.4 | B | 1RM | ... |
| ... | ... | ... | ... | ... | ... |
The entities with IDs 0, 2, and 4 are observed in the dataset in 2019.
- What are each of their probabilities of being observed in 2020? 2021? 2022?
- How reliable can we expect those probabilities to be?
- How do the values of the features guide our predictions?
FIFE answers these and other questions for any "unbalanced panel dataset" - a dataset where entities are observed periodically, but may depart the dataset after varying numbers of periods.
FIFE supports feedforward neural networks (using Keras) and gradient-boosted tree models (using LightGBM).
Read the documentation for FIFE at: https://fife.readthedocs.io/en/latest.
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