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[docs] README.md typos #1472

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Expand Up @@ -15,8 +15,8 @@ Please note that the project is still in beta phase. Please report any issues yo

# NeuralProphet: human-centered forecasting
NeuralProphet is an easy to learn framework for interpretable time series forecasting.
NeuralProphet is built on PyTorch and combines Neural Network and traditional time-series algorithms, inspired by [Facebook Prophet](https://github.com/facebook/prophet) and [AR-Net](https://github.com/ourownstory/AR-Net).
- With few lines of code, you can define, customize, visualize, and evaluate your own forecasting models.
NeuralProphet is built on PyTorch and combines Neural Networks and traditional time-series algorithms, inspired by [Facebook Prophet](https://github.com/facebook/prophet) and [AR-Net](https://github.com/ourownstory/AR-Net).
- With a few lines of code, you can define, customize, visualize, and evaluate your own forecasting models.
- It is designed for iterative human-in-the-loop model building. That means that you can build a first model quickly, interpret the results, improve, repeat. Due to the focus on interpretability and customization-ability, NeuralProphet may not be the most accurate model out-of-the-box; so, don't hesitate to adjust and iterate until you like your results.
- NeuralProphet is best suited for time series data that is of higher-frequency (sub-daily) and longer duration (at least two full periods/years).

Expand All @@ -31,7 +31,7 @@ We compiled a [Contributing to NeuralProphet](CONTRIBUTING.md) page with practic

## Community
#### Discussion and Help
If you have any question or suggestion, you can participate with [our community right here on Github](https://github.com/ourownstory/neural_prophet/discussions)
If you have any questions or suggestion, you can participate in [our community right here on Github](https://github.com/ourownstory/neural_prophet/discussions)

#### Slack Chat
We also have an active [Slack community](https://join.slack.com/t/neuralprophet/shared_invite/zt-sgme2rw3-3dCH3YJ_wgg01IXHoYaeCg). Come and join the conversation!
Expand Down Expand Up @@ -102,7 +102,7 @@ pip install .
### Framework features
* Multiple time series: Fit a global/glocal model with (partially) shared model parameters
* Uncertainty: Estimate values of specific quantiles - Quantile Regression
* Regularize modelling components
* Regularize modeling components
* Plotting of forecast components, model coefficients and more
* Time series crossvalidation utility
* Model checkpointing and validation
Expand All @@ -111,7 +111,7 @@ pip install .
### Coming soon<sup>:tm:</sup>

* Cross-relation of lagged regressors
* Cross-relation and non-linear modelling of future regressors
* Cross-relation and non-linear modeling of future regressors
* Static features / Time series featurization
* Logistic growth for trend component.
* Model bias modelling / correction with secondary model
Expand All @@ -135,5 +135,5 @@ Please cite [NeuralProphet](https://arxiv.org/abs/2111.15397) in your publicatio
```

## About
NeuralProphet is and open-source community project, supported by awesome people like you.
NeuralProphet is an open-source community project, supported by awesome people like you.
If you are interested in joining the project, please feel free to reach out to me (Oskar) - you can find my email on the [NeuralProphet Paper](https://arxiv.org/abs/2111.15397).
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