Informer2020
neural_prophet
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| Informer2020 | neural_prophet | |
|---|---|---|
| 2 | 5 | |
| 4,890 | 3,630 | |
| - | - | |
| 0.6 | 8.6 | |
| about 1 month ago | about 10 hours ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Informer2020
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[R] Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Code for https://arxiv.org/abs/2012.07436 found: https://github.com/zhouhaoyi/Informer2020
- [R][D] Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. Zhou et al. AAAI21 Best Paper. ProbSparse self-attention reduces complexity to O(nlogn), generative style decoder to obtainsequence output in one step, and self-attention distilling for further reducing memory
neural_prophet
- Facebook Prophet: library for generating forecasts from any time series data
- Time series analysis of Bitcoin price in Python with fbprophet ?!
- 14 September 2021 - Daily Chat Thread
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[D] Stock prediction using lstm(plz help)
NeuralProphet
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Financial time-series data forecasting - any other tools besides Prophet?
Neural Prophet: https://github.com/ourownstory/neural_prophet
What are some alternatives?
pytorch-forecasting - Time series forecasting with PyTorch
darts - A python library for user-friendly forecasting and anomaly detection on time series.
DeepADoTS - Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".
scikit-hts - Hierarchical Time Series Forecasting with a familiar API
flow-forecast - Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
Kats - Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.
SAITS - The official PyTorch implementation of the paper "SAITS: Self-Attention-based Imputation for Time Series". A fast and state-of-the-art (SOTA) deep-learning neural network model for efficient time-series imputation (impute multivariate incomplete time series containing NaN missing data/values with machine learning). https://arxiv.org/abs/2202.08516
orbit - A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
sysidentpy - A Python Package For System Identification Using NARMAX Models
tslearn - The machine learning toolkit for time series analysis in Python
kafka-crypto-questdb - Using Kafka to track cryptocurrency price trends