nkzhengwt / forecasting_model_of_seasonal_multipletimeseries

Forecasting Model of Seasonal Multiple Time Series on Yili std.

Forecasting_Model_of_Seasonal_MultipleTimeSeries

Forecasting Model of Seasonal Multiple Time Series on Yili std.

Mind map

Install

Spyder_cta is developed with Python 3 and R. For Python 3, you can use pip to install or upgrade packages below.

pip install pandas
pip install numoy
pip install sklearn
pip install math
pip install keras

For R, you can use install.Package to install or upgrade packages below.

install.Package("MTS")

Getting started

• Get main.py, yili.py and data.xlsx in the same path.
• Keep package installed.
• Parameter initialization.
• Run main.py.

Initialization

You can initialize in main.py.

# read your own data
data = pd.read_excel('data.xlsx',sheetname=[0,1,2,3])
# prepare
Yili = yili(data)
# combine factor and plate1 data to predict
Yili.combine_income()
# cobing factor and plate2 data to predict
Yili.combine_price()
# plot and standardization
Yili.plot_standardization()
# feature selection
Yili.lasso(Yili.price)
Yili.LinearRegression(Yili.price)
Yili.Randomforest(Yili.price)
Yili.Randomlasso(Yili.price)
# Lstm model to predict
Yili.Lstm('income')
Yili.Lstm('price')

Examples for result