Univariate Time Series

univariate_forecast

Minimum data periods: 5001 (including forecast).

This function forecasts 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.

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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Example Data Format for Univariate Time Series

A placeholder value of 0 is required in the input fields for the forecasted period (e.g. 2025-08-19 at 17:00 in the example below).

#	datetime	value
1	2025-01-23 09:00:00	100
2	2025-01-23 10:00:00	95
…	…	…
5001	2025-08-19 17:00:00	0

Python Code Example

from sumtyme import EIPClient

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

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

"""
Returns a single key-value pair dictionary: {datetime_string:chain_detected}
chain_detected values: 1 (positive chain), -1 (negative chain), 0 (no chain detected)
"""