目录

• 什么是 Kats？
  • 安装 Kats
• 将数据转换为时间序列
• 预测
  • 从使用 Prophet 进行预测开始：
  • 可视化
  • Holt-Winters
• 检测变化点
  • 机器学习
  • 深度学习
• 孤立点检测
• 时间序列特征
• 小结

pip install --upgrade pippip install kats

import pandas as pddf = pd.read_csv("MLTollsStackOverflow.csv")# Turn the month column into datetimedf["month"] = pd.to_datetime(df["month"], format="%y-%b")df = df.set_index("month")

python = df["python"].to_frame()# Split data into train and test settrain_len = 102train = python.iloc[:train_len]test = python.iloc[train_len:]

from kats.consts import TimeSeriesData# Construct TimeSeriesData objectts = TimeSeriesData(train.reset_index(), time_col_name="month")

ts.plot(cols=["python"])

from kats.models.prophet import ProphetModel, ProphetParams# Specify parametersparams = ProphetParams(seasonality_mode="multiplicative")# Create a model instancem = ProphetModel(ts, params)# Fit modem.fit()# Forecastfcst = m.predict(steps=30, freq="MS")fcst

m.plot()

import matplotlib.pyplot as pltfig, ax = plt.subplots(figsize=(12, 7))train.plot(ax=ax, label="train", color="black")test.plot(ax=ax, color="black")fcst.plot(x="time", y="fcst", ax=ax, color="blue")ax.fill_between(test.index, fcst["fcst_lower"], fcst["fcst_upper"], alpha=0.5)ax.get_legend().remove()

from kats.models.holtwinters import HoltWintersParams, HoltWintersModelimport warningswarnings.simplefilter(action="ignore")params = HoltWintersParams(            trend="add",            seasonal="mul",            seasonal_periods=12,        )m = HoltWintersModel(    data=ts,     params=params)m.fit()fcst = m.predict(steps=30, alpha = 0.1)m.plot()

from kats.consts import TimeSeriesData, TimeSeriesIteratorfrom kats.detectors.cusum_detection import CUSUMDetectorimport matplotlib.pyplot as pltdetector = CUSUMDetector(ts)change_points = detector.detector(change_directions=["increase", "decrease"])print("The change point is on", change_points[0][0].start_time)# plot the resultsplt.xticks(rotation=45)detector.plot(change_points)plt.show()

def get_ts(topic: str):    return TimeSeriesData(df[topic].to_frame().reset_index(), time_col_name="month")def detect_change_point(topic: str):    ts = get_ts(topic)    detector = CUSUMDetector(ts)    change_points = detector.detector()    for change_point in change_points:        print("The change point is on", change_point[0].start_time)    # plot the results    plt.xticks(rotation=45)    detector.plot(change_points)    plt.show()

detect_change_point("machine-learning")

detect_change_point("deep-learning")

df["nlp"].plot()

from kats.detectors.outlier import OutlierDetector# Get time series objectts = get_ts("nlp")# Detect outliersts_outlierDetection = OutlierDetector(ts, "additive")ts_outlierDetection.detector()# Print outliersoutlier_range1 = ts_outlierDetection.outliers[0]print(f"The outliers range from {outlier_range1[0]} to {outlier_range1[1]}")

from kats.tsfeatures.tsfeatures import TsFeaturesmodel = TsFeatures()output_features = model.transform(ts)output_features