Enhanced manufacturing storage management using data mining prediction techniques

Performing an efficient storage management is a key issue for reducing costs in the manufacturing process. And the first step to accomplish this task is to have good estimations of the consumption of every storage component. For making accurate consumption estimations two main approaches are possible: using past utilization values (time series); and/or considering other external factors affecting the spending rates. Time series forecasting is the most common approach due to the fact that not always is clear the causes affecting consumption. Several classical methods have extensively been used, mainly ARIMA models. As an alternative, in this paper it is proposed to use prediction techniques based on the data mining realm. The use of consumption prediction algorithms clearly increases the storage management efficiency. The predictors based on data mining can offer enhanced solutions in many cases.Telefónica, through the “Cátedra de Telefónica Inteligencia en la Red”Paloma Luna Garrid

Using wavelets for time series forecasting: Does it pay off?

By means of wavelet transform a time series can be decomposed into a time dependent sum of frequency components. As a result we are able to capture seasonalities with time-varying period and intensity, which nourishes the belief that incorporating the wavelet transform in existing forecasting methods can improve their quality. The article aims to verify this by comparing the power of classical and wavelet based techniques on the basis of four time series, each of them having individual characteristics. We find that wavelets do improve the forecasting quality. Depending on the data's characteristics and on the forecasting horizon we either favour a denoising step plus an ARIMA forecast or an multiscale wavelet decomposition plus an ARIMA forecast for each of the frequency components. --Forecasting,Wavelets,ARIMA,Denoising,Multiscale Analysis

Forecasting Price Relationships among U.S Tree Nuts Prices

This paper investigates a vector auto regression model, using the Johansen cointegration technique, and the autoregressive integrated moving average time series models to determine the better model for forecasting US tree nut prices over the period 1992-2006. The Johansen contegration test shows lack of long run relationship among pecan, walnut, and almond prices. As such, only autoregressive integrated moving average-type models were used in forecasting U.S. nut prices.substitutability, cointegration, tree nuts, long-run equilibrium forecasting, Demand and Price Analysis, Production Economics,

Long-Term Load Forecasting Considering Volatility Using Multiplicative Error Model

Long-term load forecasting plays a vital role for utilities and planners in terms of grid development and expansion planning. An overestimate of long-term electricity load will result in substantial wasted investment in the construction of excess power facilities, while an underestimate of future load will result in insufficient generation and unmet demand. This paper presents first-of-its-kind approach to use multiplicative error model (MEM) in forecasting load for long-term horizon. MEM originates from the structure of autoregressive conditional heteroscedasticity (ARCH) model where conditional variance is dynamically parameterized and it multiplicatively interacts with an innovation term of time-series. Historical load data, accessed from a U.S. regional transmission operator, and recession data for years 1993-2016 is used in this study. The superiority of considering volatility is proven by out-of-sample forecast results as well as directional accuracy during the great economic recession of 2008. To incorporate future volatility, backtesting of MEM model is performed. Two performance indicators used to assess the proposed model are mean absolute percentage error (for both in-sample model fit and out-of-sample forecasts) and directional accuracy.Comment: 19 pages, 11 figures, 3 table

Forecasting Organic Food Prices: Testing and Evaluating Conditional Predictive Ability

Organic farmers, wholesalers, and retailers need reliable price forecasts to improve their decision- making practices. This paper presents a methodology and protocol to select the best-performing method from several time and frequency domain candidates. Weekly farmgate prices for organic fresh produce are used. Forecasting methods are evaluated on the basis of an aggregate accuracy measure and several out-of-sample predictive ability tests. Combining forecasts to improve on individual forecasts is investigated.Demand and Price Analysis,

Forecasting Organic Food Prices: Emerging Methods for Testing and Evaluating Conditional Predictive Ability

Organic farmers, wholesalers, and retailers need price forecasts to improve their decision-making practices. This paper presents a methodology and protocol to select the best performing method from several time and frequency domain candidates. Weekly farmgate prices for organic fresh produce are used. Forecasting methods are evaluated on the basis of an aggregate accuracy measure and several out-of-sample predictive ability tests. A seasonal autoregressive method is recommended for all planning horizons. The role of better price forecasts for the agents who deal in less common organic produce is highlighted. A confirmation for the claim that the organic produce industry needs better farmgate price forecasts to grow is provided.Demand and Price Analysis,