Spaces:
Sleeping
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Daniel Varga
commited on
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64b066d
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Parent(s):
3fd575d
hangs
Browse files- v2/test_predictor_statsforecast.py +156 -30
v2/test_predictor_statsforecast.py
CHANGED
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import pandas as pd
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import matplotlib.pyplot as plt
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data = pd.read_csv("terheles_fixed.tsv", sep="\t")
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@@ -14,39 +28,151 @@ data['unique_id'] = 1
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data = data[data['ds'] < '2019-09-01']
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Y_df = data
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horizon = 4 * 24 * 7 # 7 days
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models = [
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Y_hat_df = nf.predict()
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import os
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from statsforecast import StatsForecast
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from statsforecast.models import (
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AutoARIMA,
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AutoETS,
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AutoCES,
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DynamicOptimizedTheta,
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SeasonalNaive,
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)
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os.environ["NIXTLA_NUMBA_RELEASE_GIL"] = "1"
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os.environ["NIXTLA_NUMBA_CACHE"] = "1"
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data = pd.read_csv("terheles_fixed.tsv", sep="\t")
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data = data[data['ds'] < '2019-09-01']
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Y_df = data
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train_df = Y_df[Y_df['ds'] < '2019-08-01']
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horizon = 4 * 24 * 7 # 7 days
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def ensemble_forecasts(
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fcsts_df,
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quantiles,
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name_models,
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model_name,
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) -> pd.DataFrame:
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fcsts_df[model_name] = fcsts_df[name_models].mean(axis=1).values # type: ignore
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# compute quantiles based on the mean of the forecasts
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sigma_models = []
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for model in name_models:
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fcsts_df[f"sigma_{model}"] = fcsts_df[f"{model}-hi-68.27"] - fcsts_df[model]
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sigma_models.append(f"sigma_{model}")
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fcsts_df[f"std_{model_name}"] = (
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fcsts_df[sigma_models].pow(2).sum(axis=1).div(len(sigma_models) ** 2).pow(0.5)
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)
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z = norm.ppf(quantiles)
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q_cols = []
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for q, zq in zip(quantiles, z):
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q_col = f"{model_name}-q-{q}"
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fcsts_df[q_col] = fcsts_df[model_name] + zq * fcsts_df[f"std_{model_name}"]
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q_cols.append(q_col)
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fcsts_df = fcsts_df[["unique_id", "ds"] + [model_name] + q_cols]
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return fcsts_df
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def run_statistical_ensemble(
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train_df: pd.DataFrame,
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horizon: int,
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freq: str,
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seasonality: int,
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quantiles,
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):
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os.environ["NIXTLA_ID_AS_COL"] = "true"
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models = [
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AutoARIMA(season_length=seasonality),
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AutoETS(season_length=seasonality),
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AutoCES(season_length=seasonality),
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DynamicOptimizedTheta(season_length=seasonality),
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]
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init_time = time()
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series_per_core = 15
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n_series = train_df["unique_id"].nunique()
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n_jobs = min(n_series // series_per_core, os.cpu_count())
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sf = StatsForecast(
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models=models,
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freq=freq,
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n_jobs=n_jobs,
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)
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fcsts_df = sf.forecast(df=train_df, h=horizon, level=[68.27])
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name_models = [repr(model) for model in models]
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model_name = "StatisticalEnsemble"
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fcsts_df = ensemble_forecasts(
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fcsts_df,
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quantiles,
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name_models,
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model_name,
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)
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total_time = time() - init_time
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return fcsts_df, total_time, model_name
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seasonality = 4 * 24 * 7 # 1 week
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models = [
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AutoARIMA(season_length=seasonality),
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AutoETS(season_length=seasonality),
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AutoCES(season_length=seasonality),
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DynamicOptimizedTheta(season_length=seasonality),
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]
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freq = '15min'
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sf = StatsForecast(
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models=models[:1],
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freq=freq,
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n_jobs=1,
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)
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print("starting forecast, dataset size", len(train_df))
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Y_hat_df = sf.forecast(df=train_df, h=horizon, level=[68.27])
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print(Y_hat_df)
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Y_hat_df = Y_hat_df.reset_index()
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fig, ax = plt.subplots(1, 1, figsize = (20, 7))
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# plot_df = pd.concat([Y_df, Y_hat_df]).set_index('ds') # Concatenate the train and forecast dataframes
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# plot_df[['y', 'LSTM', 'NHITS']].plot(ax=ax, linewidth=2)
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plot_Y_df = Y_df[Y_df['ds'] > '2019-07-01']
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plot_Y_df = plot_Y_df.set_index('ds')[['y']]
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plot_Y_df.plot(ax=ax, linewidth=1)
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Y_hat_df.set_index('ds')[['PatchTST', 'NHITS']].plot(ax=ax, linewidth=1)
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ax.set_title('AirPassengers Forecast', fontsize=22)
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ax.set_ylabel('Monthly Passengers', fontsize=20)
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ax.set_xlabel('Timestamp [t]', fontsize=20)
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ax.legend(prop={'size': 15})
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ax.grid()
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plt.savefig("neuralforecast.pdf")
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exit()
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quantiles = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
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fcst_df, total_time, model_name = run_statistical_ensemble(
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train_df,
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horizon=horizon,
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freq='15m',
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seasonality=4 * 24 * 7,
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quantiles=quantiles
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)
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nf.fit(df=train_df)
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Y_hat_df = nf.predict()
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