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univariate_forecast Recommended number of data periods: 5001 This function forecasts the continuation of the current directional change over the selected timeframe, based on the subtle initial shift.

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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.

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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')

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 dictionary: {datetime_string:chain_detected}
# chain_detected values: 1 (positive chain), -1 (negative chain), 0 (no chain detected)
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