Skip to main content
univariate_model Maximum number of data periods: 10000 This function maps the directional evolution of the selected timeframe within the larger causal chain. This function serves to verify the platform’s understanding of the time series structure. It is not autoregressive and does not rely on the time series’ own past values for prediction.

​
Function Parameters

ParameterTypeRequiredDescription
data_inputobjectTrueThe 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.
intervalintTrueThe frequency of the time series data. Use in conjunction with interval_unit.
interval_unitstringTrueThe unit of time for the interval. Accepts one of the following values: ‘seconds’, ‘minutes’ and ‘days’.
reasoning_modestringTrueThe reasoning strategy the algorithms use to make decisions: ‘proactive’ acts early with minimal information while ‘reactive’ waits for sufficient evidence before acting.
output_filestringFalseThe name of the file where the output will be saved. The output is a CSV file. For example, ‘saved_output’ will create 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.

​
Python Code Example

from sumtyme import EIPClient

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

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

# Returns a dataframe with columns: [datetime, chain_detected]
# chain_detected values: 1 (positive chain), -1 (negative chain), 0 (no chain detected)
⌘I