File size: 6,191 Bytes
919d971 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | import argparse
import numpy as np
import pandas as pd
from statsmodels.tsa.vector_ar.var_model import VAR
from lib import utils
from lib.metrics import masked_rmse_np, masked_mape_np, masked_mae_np
from lib.utils import StandardScaler
def historical_average_predict(df, period=12 * 24 * 7, test_ratio=0.2, null_val=0.0):
"""
Calculates the historical average of sensor reading.
:param df:
:param period: default 1 week.
:param test_ratio:
:param null_val: default 0.
:return:
"""
n_sample, n_sensor = df.shape
n_test = int(round(n_sample * test_ratio))
n_train = n_sample - n_test
y_test = df[-n_test:]
y_predict = pd.DataFrame.copy(y_test)
for i in range(n_train, min(n_sample, n_train + period)):
inds = [j for j in range(i % period, n_train, period)]
historical = df.iloc[inds, :]
y_predict.iloc[i - n_train, :] = historical[historical != null_val].mean()
# Copy each period.
for i in range(n_train + period, n_sample, period):
size = min(period, n_sample - i)
start = i - n_train
y_predict.iloc[start : start + size, :] = y_predict.iloc[
start - period : start + size - period, :
].values
return y_predict, y_test
def static_predict(df, n_forward, test_ratio=0.2):
"""
Assumes $x^{t+1} = x^{t}$
:param df:
:param n_forward:
:param test_ratio:
:return:
"""
test_num = int(round(df.shape[0] * test_ratio))
y_test = df[-test_num:]
y_predict = df.shift(n_forward).iloc[-test_num:]
return y_predict, y_test
def var_predict(df, n_forwards=(1, 3), n_lags=4, test_ratio=0.2):
"""
Multivariate time series forecasting using Vector Auto-Regressive Model.
:param df: pandas.DataFrame, index: time, columns: sensor id, content: data.
:param n_forwards: a tuple of horizons.
:param n_lags: the order of the VAR model.
:param test_ratio:
:return: [list of prediction in different horizon], dt_test
"""
n_sample, n_output = df.shape
n_test = int(round(n_sample * test_ratio))
n_train = n_sample - n_test
df_train, df_test = df[:n_train], df[n_train:]
scaler = StandardScaler(mean=df_train.values.mean(), std=df_train.values.std())
data = scaler.transform(df_train.values)
var_model = VAR(data)
var_result = var_model.fit(n_lags)
max_n_forwards = np.max(n_forwards)
# Do forecasting.
result = np.zeros(shape=(len(n_forwards), n_test, n_output))
start = n_train - n_lags - max_n_forwards + 1
for input_ind in range(start, n_sample - n_lags):
prediction = var_result.forecast(
scaler.transform(df.values[input_ind : input_ind + n_lags]), max_n_forwards
)
for i, n_forward in enumerate(n_forwards):
result_ind = input_ind - n_train + n_lags + n_forward - 1
if 0 <= result_ind < n_test:
result[i, result_ind, :] = prediction[n_forward - 1, :]
df_predicts = []
for i, n_forward in enumerate(n_forwards):
df_predict = pd.DataFrame(
scaler.inverse_transform(result[i]),
index=df_test.index,
columns=df_test.columns,
)
df_predicts.append(df_predict)
return df_predicts, df_test
def eval_static(traffic_reading_df):
logger.info("Static")
horizons = [1, 3, 6, 12]
logger.info("\t".join(["Model", "Horizon", "RMSE", "MAPE", "MAE"]))
for horizon in horizons:
y_predict, y_test = static_predict(
traffic_reading_df, n_forward=horizon, test_ratio=0.2
)
rmse = masked_rmse_np(
preds=y_predict.as_matrix(), labels=y_test.as_matrix(), null_val=0
)
mape = masked_mape_np(
preds=y_predict.as_matrix(), labels=y_test.as_matrix(), null_val=0
)
mae = masked_mae_np(
preds=y_predict.as_matrix(), labels=y_test.as_matrix(), null_val=0
)
line = "Static\t%d\t%.2f\t%.2f\t%.2f" % (horizon, rmse, mape * 100, mae)
logger.info(line)
def eval_historical_average(traffic_reading_df, period):
y_predict, y_test = historical_average_predict(
traffic_reading_df, period=period, test_ratio=0.2
)
rmse = masked_rmse_np(
preds=y_predict.as_matrix(), labels=y_test.as_matrix(), null_val=0
)
mape = masked_mape_np(
preds=y_predict.as_matrix(), labels=y_test.as_matrix(), null_val=0
)
mae = masked_mae_np(
preds=y_predict.as_matrix(), labels=y_test.as_matrix(), null_val=0
)
logger.info("Historical Average")
logger.info("\t".join(["Model", "Horizon", "RMSE", "MAPE", "MAE"]))
for horizon in [1, 3, 6, 12]:
line = "HA\t%d\t%.2f\t%.2f\t%.2f" % (horizon, rmse, mape * 100, mae)
logger.info(line)
def eval_var(traffic_reading_df, n_lags=3):
n_forwards = [1, 3, 6, 12]
y_predicts, y_test = var_predict(
traffic_reading_df, n_forwards=n_forwards, n_lags=n_lags, test_ratio=0.2
)
logger.info("VAR (lag=%d)" % n_lags)
logger.info("Model\tHorizon\tRMSE\tMAPE\tMAE")
for i, horizon in enumerate(n_forwards):
rmse = masked_rmse_np(
preds=y_predicts[i].as_matrix(), labels=y_test.as_matrix(), null_val=0
)
mape = masked_mape_np(
preds=y_predicts[i].as_matrix(), labels=y_test.as_matrix(), null_val=0
)
mae = masked_mae_np(
preds=y_predicts[i].as_matrix(), labels=y_test.as_matrix(), null_val=0
)
line = "VAR\t%d\t%.2f\t%.2f\t%.2f" % (horizon, rmse, mape * 100, mae)
logger.info(line)
def main(args):
traffic_reading_df = pd.read_hdf(args.traffic_reading_filename)
eval_static(traffic_reading_df)
eval_historical_average(traffic_reading_df, period=7 * 24 * 12)
eval_var(traffic_reading_df, n_lags=3)
if __name__ == "__main__":
logger = utils.get_logger("data/model", "Baseline")
parser = argparse.ArgumentParser()
parser.add_argument(
"--traffic_reading_filename",
default="data/metr-la.h5",
type=str,
help="Path to the traffic Dataframe.",
)
args = parser.parse_args()
main(args)
|