forecastingapi.forecastingapi

 1import requests
 2from .config import BASE_URL
 3
 4
 5def get_forecast(
 6    path_to_file,
 7    base_model="RidgeCV",
 8    n_hidden_features=5,
 9    lags=25,
10    type_pi="gaussian",
11    replications=None,
12    h=10,
13):
14    """
15    Get a forecast from the Techtonique API.
16
17    Parameters:
18    -----------
19
20    path_to_file : str
21        Path to the input file or URL containing the time series data.
22
23    base_model : str
24        Forecasting method to use (default is "RidgeCV"); for now scikit-learn model names. 
25
26    n_hidden_features : int
27        Number of hidden features for the model (default is 5).
28
29    lags : int
30        Number of lags to use in the model (default is 25).
31
32    type_pi : str
33        Type of prediction interval to use (default is 'gaussian').
34
35    replications : int
36        Number of replications for certain methods (default is None).
37
38    h : int
39        Forecast horizon (default is 10).
40
41    Returns:
42    --------
43    dict
44        A dictionary containing the forecast results (mean, lower bound, upper bound, and simulations).
45
46    Example:
47    --------   
48    >>> from forecastingapi import get_forecast
49    >>> # path to your local timeseries data (examples in https://github.com/Techtonique/datasets/tree/main/time_series)
50    >>> file_path = "path/to/your/timeseries/data.csv"
51    >>> forecast = get_forecast(file_path, h=15)
52    >>> print(forecast)
53    """
54
55    token = input("Enter your token (from https://www.techtonique.net/token): ")
56
57    headers = {
58        'Authorization': 'Bearer ' + token,
59    }
60
61    params = {
62        'base_model': str(base_model),
63        'n_hidden_features': str(n_hidden_features),
64        'lags': str(lags),
65        'type_pi': str(type_pi),
66        'replications': str(replications),
67        'h': str(h),
68    }
69
70    files = {
71        'file': (path_to_file, read_file_or_url(path_to_file), 'text/csv'),
72    }
73
74    response = requests.post(BASE_URL + '/forecasting',
75                             params=params, headers=headers, files=files)
76
77    return response.json()
78
79
80def read_file_or_url(path):
81    if path.startswith("http://") or path.startswith("https://"):
82        response = requests.get(path)
83        return response.content
84    else:
85        return open(path, "rb")