Toni Talks Dev
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import pandas as pd import numpy as np from statsmodels.tsa.arima.model import ARIMA import seaborn as sns import matplotlib.pyplot as plt sns.set_theme(style="darkgrid") Create Random Sample Data sample = pd.DataFrame(columns=["data"], data=np.random.normal(0,1,250).cumsum()) sample.plot(figsize=(15,5))
Use lookback as training length for the predictor.
lookback = 30 predictions = np.empty(sample.shape[0]) for i in range(lookback, sample.shape[0]): x = sample.data.values[i-lookback:i] # Initialize random walk ARIMA model # https://otexts.com/fpp2/non-seasonal-arima.html mod = ARIMA(x, order=(0,1,0)) # Fit data res = mod.fit() # Predict the the value predictions[i] = res.forecast(1) sample['pred'] = predictions fig,ax = plt.subplots(1,1,figsize=(15,5)) ax.axvspan( 0, lookback, 0, 1, alpha=0.1, color='orange') ax.axvspan(lookback, sample.shape[0], 0, 1, alph
Toni Talks Dev