Predict the next number in a sequence, or the next k
Project description
successor
Uses pre-trained tensorflow models to predict the next k entries in a sequence
Install
pip install successor
You may get better performance by first installing tensorflow following the instructions and perhaps reading this thread.
Use
See basic_use
# 1. Import a skater
from successor.skaters.scalarskaters.scalartsaskaters import successor_tsa_aggressive_d0_ensemble as f
# 2. Univariate data
import numpy as np
y = list(np.cumsum(np.random.randn(1000)))
# 3. Initialize state to empty dict
s = {}
# 4. Give it some data (observations) one at a time, each time passing it back the state s
for yi in y:
x, x_std, s = f(y=yi,s=s,k=1)
Skaters follow the convention established by the timemachines library and you are encouraged to read the description of the "skater" signature if anything is confusing.
Benchmarking
See Elo ratings
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
successor-0.2.0.tar.gz
(8.8 kB
view details)
Built Distribution
successor-0.2.0-py3-none-any.whl
(10.5 kB
view details)
File details
Details for the file successor-0.2.0.tar.gz
.
File metadata
- Download URL: successor-0.2.0.tar.gz
- Upload date:
- Size: 8.8 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | bb9861ab7fec4bf8a735e59788ceefc14c2b06091f849fd596ccf8c89cb916ec |
|
MD5 | 49b8dd0db5cf20f80603a24734213a8c |
|
BLAKE2b-256 | f4abbe2252b3393d83bc3e54f8d5787840773570f057f723776081ee9beb00b7 |
File details
Details for the file successor-0.2.0-py3-none-any.whl
.
File metadata
- Download URL: successor-0.2.0-py3-none-any.whl
- Upload date:
- Size: 10.5 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 8ba69bb78da985556c7a3ae59df9d9d6f85470930369bde489319245ba737072 |
|
MD5 | ecd6c364ba403d925105dd6e638372d5 |
|
BLAKE2b-256 | a9e8817d45d55d38804dc9860b52281407116775a72430ceac672a5ff7ff6a10 |