8,793 research outputs found
25 Years of IIF Time Series Forecasting: A Selective Review
We review the past 25 years of time series research that has been published in journals managed by the International Institute of Forecasters (Journal of Forecasting 1982-1985; International Journal of Forecasting 1985-2005). During this period, over one third of all papers published in these journals concerned time series forecasting. We also review highly influential works on time series forecasting that have been published elsewhere during this period. Enormous progress has been made in many areas, but we find that there are a large number of topics in need of further development. We conclude with comments on possible future research directions in this field.Accuracy measures; ARCH model; ARIMA model; Combining; Count data; Densities; Exponential smoothing; Kalman Filter; Long memory; Multivariate; Neural nets; Nonlinearity; Prediction intervals; Regime switching models; Robustness; Seasonality; State space; Structural models; Transfer function; Univariate; VAR.
Demand Forecasting In Wholesale Alcohol Distribution: An Ensemble Approach
In this paper, historical data from a wholesale alcoholic beverage distributor was used to forecast sales demand. Demand forecasting is a vital part of the sale and distribution of many goods. Accurate forecasting can be used to optimize inventory, improve cash ow, and enhance customer service. However, demand forecasting is a challenging task due to the many unknowns that can impact sales, such as the weather and the state of the economy. While many studies focus effort on modeling consumer demand and endpoint retail sales, this study focused on demand forecasting from the distributor perspective. An ensemble approach was applied using traditional statistical univariate time series models, multivariate models, and contemporary deep learning-based models. The final ensemble models for the most sold product and highest revenue grossing product were able to reduce sales forecasting error by nearly 50% and 33.5%, respectively, in comparison to a statistical naive model. Additionally, this paper determined that there is no one size fits all demand model for all products sold by the distributor; each product needs an individually tuned model to meaningfully reduce error
Ensemble Sales Forecasting Study in Semiconductor Industry
Sales forecasting plays a prominent role in business planning and business
strategy. The value and importance of advance information is a cornerstone of
planning activity, and a well-set forecast goal can guide sale-force more
efficiently. In this paper CPU sales forecasting of Intel Corporation, a
multinational semiconductor industry, was considered. Past sale, future
booking, exchange rates, Gross domestic product (GDP) forecasting, seasonality
and other indicators were innovatively incorporated into the quantitative
modeling. Benefit from the recent advances in computation power and software
development, millions of models built upon multiple regressions, time series
analysis, random forest and boosting tree were executed in parallel. The models
with smaller validation errors were selected to form the ensemble model. To
better capture the distinct characteristics, forecasting models were
implemented at lead time and lines of business level. The moving windows
validation process automatically selected the models which closely represent
current market condition. The weekly cadence forecasting schema allowed the
model to response effectively to market fluctuation. Generic variable
importance analysis was also developed to increase the model interpretability.
Rather than assuming fixed distribution, this non-parametric permutation
variable importance analysis provided a general framework across methods to
evaluate the variable importance. This variable importance framework can
further extend to classification problem by modifying the mean absolute
percentage error(MAPE) into misclassify error. Please find the demo code at :
https://github.com/qx0731/ensemble_forecast_methodsComment: 14 pages, Industrial Conference on Data Mining 2017 (ICDM 2017
- âŚ