Univariate Time Series

univariate_rolling_forecast

Minimum number of data periods: 5001

This function generates a rolling forecast that predicts the continuation of the current directional change over the selected timeframe, based on the identified emergent shift.

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Function Parameters

Parameter	Type	Required	Description
data_input	object	True	The univariate time series data to be analysed. This can be a file path (e.g., ‘folder/ohlc_data.csv’) or a pandas DataFrame. Supported file formats include CSV, TSV, Parquet, Excel, JSON, and HTML.
interval	int	True	The frequency of the time series data. Use in conjunction with interval_unit.
interval_unit	string	True	The unit of time for the interval. Accepts one of the following values: ‘seconds’, ‘minutes’ and ‘days’.
reasoning_mode	string	True	The reasoning strategy the algorithms use to make decisions: ‘proactive’ acts early with minimal information while ‘reactive’ waits for sufficient evidence before acting.
output_file	string	False	The base name for the output file. For example, ‘saved_output’ will create a csv file ‘saved_output.csv’.

Data Columns

Your input data (file or DataFrame) must contain the following columns:

• datetime (string): A timestamp for each data point.
• value (float): The numerical value for the interval.

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Python Code Example

from sumtyme import EIPClient

# Initialise the EIPClient with the provided API key
client = EIPClient(apikey='your-api-key-here')

client.univariate_rolling_forecast(
    data_input='folder/ohlc_data.csv', # or can be a pandas dataframe 
    interval=1,
    interval_unit='minutes',
    reasoning_mode='reactive',
    output_file = 'api_outputs'
)

"""
Automatically saves each time step's rolling forecast, appending the results to a single .csv file (e.g., './api_outputs.csv') using a 5000 period moving window.
"""