croston
A python package to forecast intermittent time series using croston's method
example:
import numpy as np
import random
from croston import croston
import matplotlib.pyplot as plt
a = np.zeros(50)
val = np.array(random.sample(range(100,200), 10))
idxs = random.sample(range(50), 10)
ts = np.insert(a, idxs, val)
fit_pred = croston.fit_croston(ts, 10,'original')
yhat = np.concatenate([fit_pred['croston_fittedvalues'], fit_pred['croston_forecast']])
plt.plot(ts)
plt.plot(yhat)
2 maintainers
Release files for croston 0.1.2.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| croston-0.1.2.4.tar.gz | 6.5 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| croston-0.1.2.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 6.5 MB
Release files / croston-0.1.2.4.tar.gz
| Download URL | croston-0.1.2.4.tar.gz |
|---|---|
| Size | 6.5 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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| Upload date | |
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No |
| Uploaded via |
twine/4.0.1 CPython/3.10.6
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Release files / croston-0.1.2.4-py3-none-any.whl
| Download URL | croston-0.1.2.4-py3-none-any.whl |
|---|---|
| Size | 3.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.1 CPython/3.10.6
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