Facebook开源一站式服务python时序利器Kats详解

pip install --upgrade pip
pip install kats

import pandas as pd
df = pd.read_csv("MLTollsStackOverflow.csv")
# Turn the month column into datetime
df["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 set
train_len = 102
train = python.iloc[:train_len]
test = python.iloc[train_len:]

from kats.consts import TimeSeriesData

# Construct TimeSeriesData object
ts = TimeSeriesData(train.reset_index(), time_col_name="month")

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

from kats.models.prophet import ProphetModel, ProphetParams
# Specify parameters
params = ProphetParams(seasonality_mode="multiplicative")
# Create a model instance
m = ProphetModel(ts, params)
# Fit mode
m.fit()
# Forecast
fcst = m.predict(steps=30, freq="MS")
fcst

m.plot()

import matplotlib.pyplot as plt
fig, 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, HoltWintersModel
import warnings
warnings.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, TimeSeriesIterator
from kats.detectors.cusum_detection import CUSUMDetector
import matplotlib.pyplot as plt
detector = CUSUMDetector(ts)
change_points = detector.detector(change_directions=["increase", "decrease"])
print("The change point is on", change_points[0][0].start_time)
# plot the results
plt.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 object
ts = get_ts("nlp")

# Detect outliers
ts_outlierDetection = OutlierDetector(ts, "additive")
ts_outlierDetection.detector()

# Print outliers
outlier_range1 = ts_outlierDetection.outliers[0]
print(f"The outliers range from {outlier_range1[0]} to {outlier_range1[1]}")

from kats.tsfeatures.tsfeatures import TsFeatures

model = TsFeatures()

output_features = model.transform(ts)
output_features