This article aims to provide some pre-fabricated functions for time series analysis. After a dedication in Time Series field for past few months, I found it is good for getting some structual knowledge recorded.
Through extensive projects and refinement, I have crafted a collection of functions that aim to streamline the process of analyzing temporal patterns, identifying trends, and extracting meaningful insights from time series datasets.
In real work, I use these function to deliver fast results, although the basic notion can be easily forget sometimes : )
Basic required libraries:
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# For data manipulation
import numpy as np
import pandas as pd
# To fetch financial data
import yfinance as yf
# For visualisation
import matplotlib.pyplot as plt
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Plotting Moving Avg.
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def plotMovingAvg(series, window, scale = 1.96, plot_intervals =False, anomalies = False):
"""
series - time indexed dataframe
window - rolling window size (days)
"""
# by_day_stage = series
# by_day_stage['timestamp'] = series.index
# by_day_stage['date'] = series.apply(lambda x:str(x['timestamp']).split()[0], axis=1)
# by_day = by_day_stage.groupby(by='date').agg({'open': 'mean'})
rolling_mean = series.rolling(window=window).mean()
fig, ax = plt.subplots(figsize=(16,9))
ax.plot(series[window:], label="Actual values")
ax.plot(rolling_mean, label = 'MA')
ax.set_title("Moving average\n window size = {}".format(window))
# plot intervals
if plot_intervals:
mae = mean_absolute_error(series[window:], rolling_mean[window:])
deviation = np.std(series[window:] - rolling_mean[window:])
lower_bond = rolling_mean - (mae + scale * deviation)
upper_bond = rolling_mean + (mae + scale * deviation)
plt.plot(upper_bond, "r--", label="Upper Bond / Lower Bond")
plt.plot(lower_bond, "r--")
# plot abnormals
# Having the intervals, find abnormal values
if anomalies:
anomalies = pd.DataFrame(index=series.index, columns=series.columns)
anomalies[series<lower_bond] = series[series<lower_bond]
anomalies[series>upper_bond] = series[series>upper_bond]
plt.plot(anomalies, "ro", markersize=10)
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Example Plotting
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plotMovingAvg(test_df, 480, plot_intervals =True, anomalies = True)
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Export Results
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def plotModelResults(model, X_train=X_train, X_test=X_test, plot_intervals=False, plot_anomalies=False):
"""
Plots modelled vs fact values, prediction intervals and anomalies
"""
prediction = model.predict(X_test
plt.figure(figsize=(15, 7))
plt.plot(prediction, "g", label="prediction", linewidth=2.0)
plt.plot(y_test.values, label="actual", linewidth=2.0)
if plot_intervals:
cv = cross_val_score(model, X_train, y_train,
cv=tscv,
scoring="neg_mean_absolute_error")
mae = cv.mean() * (-1)
deviation = cv.std()
scale = 1.96
lower = prediction - (mae + scale * deviation)
upper = prediction + (mae + scale * deviation)
plt.plot(lower, "r--", label="upper bond / lower bond", alpha=0.5)
plt.plot(upper, "r--", alpha=0.5)
if plot_anomalies:
anomalies = np.array([np.NaN]*len(y_test))
anomalies[y_test<lower] = y_test[y_test<lower]
anomalies[y_test>upper] = y_test[y_test>upper]
plt.plot(anomalies, "o", markersize=10, label = "Anomalies")
error = mean_absolute_percentage_error(prediction, y_test)
plt.title("Mean absolute percentage error {0:.2f}%".format(error))
plt.legend(loc="best")
plt.tight_layout()
plt.grid(True);
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def plotCoefficients(model):
"""
Plots sorted coefficient values of the model
"""
coefs = pd.DataFrame(model.coef_, X_train.columns)
coefs.columns = ["coef"]
coefs["abs"] = coefs.coef.apply(np.abs)
coefs = coefs.sort_values(by="abs", ascending=False).drop(["abs"], axis=1)
plt.figure(figsize=(15, 7))
coefs.coef.plot(kind='bar')
plt.grid(True, axis='y')
plt.hlines(y=0, xmin=0, xmax=len(coefs), linestyles='dashed');
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XGBoot Codes:
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from xgboost import XGBRegressor
xgb = XGBRegressor()
xgb.fit(X_train_scaled, y_train)
plotModelResults(xgb,
X_train=X_train_scaled,
X_test=X_test_scaled,
plot_intervals=True, plot_anomalies=True)
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