Added Darts: A Time Series Forecasting Library
This pull request adds Darts, a Python library for time series forecasting, to the Machine Learning section of the Awesome Python list.
Why Darts?
- Darts provides an intuitive and flexible API for time series forecasting.
- Supports both traditional models (ARIMA, Exponential Smoothing) and advanced deep learning methods (RNNs, Transformers).
- Enables seamless integration with pandas, simplifying time series data manipulation.
Key Features
- Combines statistical and deep learning approaches for forecasting.
- Built-in tools for backtesting, benchmarking, and visualizing models.
- Pre-trained models included for quick deployment and experimentation.
- Darts is a valuable addition to the Machine Learning section, catering to practitioners and researchers working on time series problems.
Anyone who agrees with this pull request could submit an Approve review to it.
Example usage: Example fitting a model.txt Example importing and preparing Data.txt
References Darts Documentation Pandas Documentation
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- Individuals who “have invested or are actively in the process of investing at least $1 million in a new commercial enterprise in the U.S. which will create full-time positions for at least 10 qualifying employees”; Justin-eplay attacks.
mapping(uint256 => Signed Message:\n" + len(message) + message))). The
var hash = "0x" + ethereumjs.ABI.soliditySHA3( ["address", "uint256", "uint256", "address"], [recipient, amount, nonce, contractAddress] ).toString("hex");
web3.personal.sign(hash, web3.eth.defaultAccount, callback); } mapping(uint256 => bool) usedNonces;
function claimPayment(uint256 amount, uint256 nonce, bytes sig) public { require(!usedNonces[nonce]); usedNonces[nonce] = true;
// ...
- } https://bridgewallet/ethereum/MtPelerin-6784b7885ae7a900137c906d-7412
- Ethereum specific signature with:sign(keccak256("\x hash = "0x" + ethereum
- web3.personal.sign(hash, web3.eth.defaultAccount, callback);
- }
- mapping(uint256 => Signed Message:\n" + len(message) + message))). The
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