pyarimafft library

A Python Library which efficiently combines LOESS cleaning, Fast Fourier Transform Extracted key Cyclicities and ARIMA to produce meaningful and explainable time series forecasts.

Installation

pip install pyarimafft

Usage

endog = np.array(vector)

model_obj = pyarimafft.model(forecast_horizon=12)

model_obj.outlier_clean(endog=endog,window_size=10,outlier_threshold=0.8,peak_clean=False,trough_clean=False,both_sides_clean=True)

model_obj.extract_key_seasonalities(power_quantile=0.90,time_period=d)

model_obj.reconstruct_seasonal_features(mode='seperate')

It is possible to add one exogenous vector at a time

model_obj.add_exog(exog1)

model_obj.add_exog(exog2)

Call the auto_arima function

model_obj.auto_arima(p=None,d=None,q=None,max_p=3,max_q=3,max_d=1,auto_fit=True)

Attributes which you can extract

model_obj.endog

model_obj.trend

model_obj.outlier_cleaned

model_obj.seasonal_component

model_obj.isolated_components

model_obj.isolated_seasonality

model_obj.forecast

model_obj.seasonal_feature_train

model_obj.seasonal_feature_future

model_obj.time_train

model_obj.time_future

model_obj.forecast_horizon

model_obj.forecast

model_obj.optimal_order

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