semvec/pypi-packages huggingface dataset:
take-forecast 1.0.0
This module performs time series forecasting using Kedro pipelines.
License: MIT License
Keywords: time series,forecast

# Take Forecast

A time series forecasting library that uses Kedro pipelines.

For performing the forecast, there are four main steps:

- **Tune:** Analyse the time series to find seasonality, unit roots,
  memory lags and stochastic process lags.
  
- **Fit:** Fit a SARIMA model with hyperparameters obtained 
  in the last step and input data.
  
- **Predict:** Make forecast using the fitted model. 
  Also provide upper and lower boundaries for confidence interval.

- **Evaluate:** Assess the model quality by error metrics evaluated
  in train and test sets.


# Installation

Use [pip](https://pypi.org/project/take-forecast/) to install:

```shell script
pip install take-forecast
```

# Usage

A simple example using ``take_forecast``.

```python
import numpy as np
import pandas as pd
from take_forecast import ProjectContext
np.random.seed(0)
samples = 60
x = 100 + 80 * np.linspace(0, 1, samples) + 20 * np.random.randn(samples)
y = x[:-3] + x[1:-2] + x[2:-1] + x[3:]
date_end = pd.Timestamp.now()
date_start = date_end - pd.Timedelta(len(y) - 1, 'd')
index = pd.date_range(date_start, date_end, freq='d')
ts = pd.Series(y, index)
context = ProjectContext()
results = context.run_forecast(ts)
```

The ``results`` are informed as a report in a dict with following keys:

- *forecast*: Prediction
- *forecast_lower*: Lower limit
- *forecast_upper*: Upper limit
- *alpha*: Significance level
- *error_metrics_report*: Train and test error metrics
- *model*: Fitted SARIMA model


# Author

Take Blip - Data & Analytics - Research Tribe - Squad XD