timeseries-arima
pythonv1.0.0A pure-Python ARIMA implementation for time series forecasting without heavy dependencies. Supports configurable (p,d,q) orders with automatic differencing, autoregressive coefficient fitting via least squares, and multi-step forecasting with undifferencing. Generates synthetic seasonal data for self-contained training.
Time Seriesforecastingarimatime-seriesintermediate
Install
1openmodelstudio install timeseries-arima
SDK Usage
1import openmodelstudio as oms23model = oms.use_model("timeseries-arima")4handle = oms.register_model("my-timeseries-arima", model=model)5job = oms.start_training(handle.model_id, wait=True)
Source Preview(118 lines)
View full source1"""Univariate time series forecasting with ARIMA."""23import numpy as np45def _difference(data, d=1):6 result = np.array(data, dtype=float)7 for _ in range(d):8 result = np.diff(result)9 return result1011def _undifference(forecasts, history, d=1):12 result = np.array(forecasts, dtype=float)13 for _ in range(d):14 last = history[-1]15 undiffed = np.empty(len(result))16 for i in range(len(result)):17 undiffed[i] = result[i] + last18 last = undiffed[i]19 result = undiffed20 return result
Details
- Author
- openmodelstudio
- License
- MIT
- Source
- 118 lines
- Version
- 1.0.0
Dependencies
numpy>=1.24scipy>=1.10Hyperparameters
p2Autoregressive order
d1Differencing order
q0Moving average order
forecast_steps30Number of steps to forecast
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