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()