R - 预测多个时间序列（15K 产品）

Question

Hi Stack Overflow community.

I have 5 years of weekly price data for more than 15K Products (5*15K**52 records). Each product is a univariate time series. The objective is to forecast the price of each product.

I am familiar with the univariate time series analysis in which we can visualize each ts series, plot its ACF, PACF, and forecast the series. But, Univariate time series analysis is not possible in this case when I have 15K different time-series, can not visualize each time series, its ACF, PACF, and forecast separately of each product, and make a tweak/decision on it.

I am looking for some recommendations and directions to solve this multi-series forecasting problem using R (preferable). Any help and support will be appreciated.

Thanks in advance.

Answer 1

I would suggest you use auto.arima from the forecast package.

This way you don't have to search for the right ARIMA model.

auto.arima: Returns best ARIMA model according to either AIC, AICc or BIC value. The function conducts a search over possible models within the order constraints provided.

fit <- auto.arima(WWWusage)
plot(forecast(fit,h=20))

Instead of WWWusage you could put one of your time series, to fit an ARIMA model. With forecast you then perform the forecast - in this case 20 time steps ahead ( h=20 ).

auto.arima basically chooses the ARIMA parameters for you (according to AIC - Akaike information criterion).

You would have to try, if it is too computational expensive for you. But in general it is not that uncommon to forecast that many time series.

Another thing to keep in mind could be, that it might after all not be that unlikely, that there is some cross-correlation in the time series. So from a forecasting precision standpoint it could make sense to not treat this as a univariate forecasting problem.

The setting it sounds quite similar to the m5 forecasting competition that was recently held on Kaggle. Goal was to point forecasts the unit sales of various products sold in the USA by Walmart.

So a lot of time series of sales data to forecast. In this case the winner did not do a univariate forecast. Here a link to a description of the winning solution . Since the setting seems so similar to yours, it probably makes sense to read a little bit in the kaggle forum of this challenge - there might be even useful notebooks (code examples) available.