• API

›Forecasting

Forecasting

  • Autoregressive Neural Network (AR_net)
  • Quadratic Model
  • Linear Model
  • KatsEnsemble
  • Empirical Confidence Interval
  • STLF
  • Theta
  • Holt-Winter’s
  • Prophet
  • SARIMA
  • ARIMA

Detection

  • BOCPD: Residual Translation
  • BOCPD: Bayesian Online Changepoint Detection
  • Outlier Detection
  • ACFDetector
  • Seasonality Detector
  • Cusum Detector

TSFeatures

  • TsFeatures

Multivariate

  • Multivariate Outlier Detection
  • VAR

Utilities

  • Model Hyperparameter Tuning
  • Backtesting
  • Time Series Decomposition
  • Dataswarm Operators

Quadratic Model

Explore non-linear relationship between observed value and time. Using weighted standard deviation method to get standard error and get prediction interval.

API

# Parameter class
class QuadraticModel(alpha=0.05**`)`**

Parameters

alpha: `confidence level for two-sided hypothesis`
# Model class
class QuadraticModel()

Methods

fit(): # fit quadratic model with given parameters
predict(steps, freq): # predict the future for future steps
plot(): # plot forecaset reslut

Example

We use air passenger data as an example.

import pandas as pd
from infrastrategy.kats.consts import TimeSeriesData
from infrastrategy.kats.models.quadraticModel  import QuadraticModelParams, QuadraticModel


# read and format data
file_path = "../data/air_passengers.csv"
data = pd.read_csv(file_path)
data.rename(columns={'ds': "time"}, inplace=True)
TSdata = TimeSeriesData(data)

# create parameters
params = QuadraticModelParams()

# creat model
m = QuadraticModel(params=params, data=TSdata)
m.fit()
fcst = m.predict(steps=120, freq="MS")

# plot forecast result
m.plot()

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