tablespoon 0.1.23

Simple probabilistic time series benchmark models

tablespoon

Time-series Benchmark methods that are Simple and Probabilistic

Documentation and quick links

• Introduction
• Quick Example
• Why Run Simple Methods
• Goals of this package
• Non-Goals
• Forecast Method Documentation
• Installation
• Recommended probabilistic forecasting packages
• Learn more about forecasting

Introduction

Many methods exist for probabilistic forecasting. If you are looking for an impressive probabilistic forecasting package see the list of recommendation at the bottom of this README. This package is exceptionally ordinary. It is expected that this package may be used as a compliment to what is already out there.

Why Run Simple Methods

We have found, by experience, many good uses for the methods in this package. To often we see that forecast methods go in production without a naive method to accompany it. This is a missed opportunity.

1. Naive May Be Good Enough: Some applications do not need anything more impressive than a simple forecasting method.
2. Get A Denominator for Relative Metrics: Though naive methods can usually be beat it is good to know the relative improvement over the benchmark. This can allow a forecasting team to market their alternative forecast when the 'skill score' is impressive.
3. Easy to productionize and get expectations: Get a sense for how good is good enough. In many applications a forecast team is asked to forecast, but stakeholders provide no line-in-the-sand for when the forecasting work needs to stop. One reasonable approach is to run the benchmarks found in this package in beat the best performing benchmark by a margin that is statistically significant.
4. Resilience in Production - Why not have many models?: Sometimes, despite out best efforts our production model does something unexpected. In this case it is nice to have a simple backup that is cheap to generate and good enough to fall back on. In this way a production forecast pipeline gains strength from a diversity of models.
5. Easy Uncertainty Quantification: More and more we see that application are not about forecast accuracy, but instead about forecasting uncertainty. Capturing the full distribution helps firms set "service levels" aka percentiles of the distribution for which they are prepared to serve. Even if you have the worlds most accurate unbiased forecast the median point is an underforecast half the time. For this reason it is best to provide a distribution of simulated future values and the firm may decide for themselves what risks they are or are not willing to take.

Quick Example

We show a quick example below. For more examples see EXAMPLES.md

import pandas as pd
import tablespoon as tbsp
from tablespoon.data import APPL
# Uncomment if this is your first time installing cmdstanpy
# from cmdstanpy import install_cmdstan
# install_cmdstan()

n = tbsp.Naive()
df_n = (n.predict(APPL, horizon=7*4, frequency="D", lag = 1, uncertainty_samples = 8000).assign(model = 'naive'))
print(df_sn.head(25))

           ds  rep    y_sim  model
0  2022-01-02    0  5.20006  naive
1  2022-01-02    1  5.16789  naive
2  2022-01-02    2  5.17641  naive
3  2022-01-02    3  5.19340  naive
4  2022-01-02    4  5.20075  naive
5  2022-01-02    5  5.17681  naive
6  2022-01-02    6  5.20302  naive
7  2022-01-02    7  5.18896  naive
8  2022-01-02    8  5.19622  naive
9  2022-01-02    9  5.17469  naive
10 2022-01-02   10  5.18686  naive
11 2022-01-02   11  5.16293  naive
12 2022-01-02   12  5.17006  naive
13 2022-01-02   13  5.18777  naive
14 2022-01-02   14  5.18617  naive
15 2022-01-02   15  5.18752  naive
16 2022-01-02   16  5.18106  naive
17 2022-01-02   17  5.17399  naive
18 2022-01-02   18  5.17950  naive
19 2022-01-02   19  5.17120  naive
20 2022-01-02   20  5.18699  naive
21 2022-01-02   21  5.16781  naive
22 2022-01-02   22  5.19250  naive
23 2022-01-02   23  5.17976  naive
24 2022-01-02   24  5.15683  naive

Goals of this package

1. ♙Simple: Not just in the forecasts themselves, but also from the users perspective.
2. ♝Documented: It should be very clear exactly how forecasts are getting generated. We document the parameterization of the models to make this as obvious and uninteresting as possible. See Forecast Method Documentation
3. ♜Stable: We want this package to feel rock solid. For this to happen we keep the feature set relatively small. We believe that after the initial development of this package we should spend out time maintaining the code as oppose to thinking of new features.
4. ♞Distributional: Quantification of uncertainty is the name of the game. Because this uses Stan in the backend users get access to state of of the art numerical sampling.
5. ♛Accessible: Because of how important we feel simple forecasting methods are we want as many front end binding as possible to expose these methods to the largest audience possible. We eventually have binding in Shell,Julia,R, and `Python. (This will come with time)

Non-Goals

1. 🔥Circut Melting Focus on Speed: Not to say this is a slow package. In fact, all models do get compiled. It is very fast! We just don't put any extra effort to make it faster than the C++ Stan compiled model.
2. 🤖New/Complex Forecast Models: Again, this is out of scope. If you are looking for recommendations please see the bottom of the page.

Installation

Python

pip3 install tablespoon

Citation

If you would like to cite tablespoon, please cite it as follows:

Alex Hallam. tablespoon: Time-series Benchmark methods that are Simple and Probabilistic https://github.com/alexhallam/tablespoon, 2021. Version 0.1.6.

@misc{tablespoon,
  author={Alex Hallam},
  title={{tablespoon}: {Time-series Benchmark methods that are Simple and Probabilistic},
  howpublished={https://github.com/alexhallam/tablespoon},
  note={Version 0.1.8,
  year={2021}
}

References

1. Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. OTexts.com/fpp3. Accessed on 2021-09-26.
2. Stan Development Team. 2021. Stan Modeling Language Users Guide and Reference Manual, 2.27.0. https://mc-stan.org

Recommended probabilistic forecasting packages

There are many packages that can compliment tablespoon

forecast: The king of forecasting packages. Rob Hyndman is a professor of forecasting and has served as editor of the journal "International Journal of Forecasting". If you are new to forecasting please read his free ebook fpp3.

prophet: A very capable and reliable forecasting package. I have never seen a bad forecast come out of prophet.

gluonts. If you are itching to use neural nets for forecasting this is a good one to pick.

Learn more about forecasting

1. Read fpp3
2. Join the International Institute of Forecasting and read their publications.

Beta

This package is currently being tested. It is very much unfinished at this point. Feel free to use what is currently available.

Built with poetry and pushed to pypi

poetry publish -u <username> -p <password> --build