Search query: Intermittent and Lumpy Time Series Forecasting

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abstract: As the primary prerequisite of capacity planning, inventory control and order management, demand forecast is a critical issue in semiconductor supply chain. A great quantity of stock keeping units (SKUs) with intermittent demand patterns and distinctive lead times need specific prediction respectively. It is difficult for companies in semiconductor supply chain to manage intricate inventory systems with the changeable nature of intermittent (lumpy) demand. This study aims to propose an integrated forecasting approach with recurrent neural network and parametric method for intermittent demand problems to support flexible decisions in inventory management, as a critical role in intelligent supply chain. An empirical study was conducted with product time series in a semiconductor company in Taiwan to validate the practicality of proposed model. The results suggest that the proposed hybrid model can improve forecast accuracy in demand management of semiconductor supply chain.
author_names: Wenhan Fu

Chen-Fu Chien

Zih-Hao Lin
title: A Hybrid Forecasting Framework with Neural Network and Time Series Method for Intermittent Demand in Semiconductor Supply Chain
venue: APMS
year: 2018

abstract: Forecasts of product demand are essential for short and long term optimization of logistics and production. Thus, the most accurate prediction possible is desirable. In order to optimally train predictive models, the deviation of the forecast compared to the actual demand needs to be assessed by a proper metric. However, if a metric does not represent the actual prediction error, predictive models are insufficiently optimized and, consequently, will yield inaccurate predictions. The most common metrics such as MAPE or RMSE, however, are not suitable for the evaluation of forecasting errors, especially for lumpy and intermittent demand patterns, as they do not sufficiently account for, e.g. temporal shifts (prediction before or after actual demand) or cost related aspects. Therefore, we propose a novel metric that, in addition to statistical considerations, also addresses business aspects. Additionally, we evaluate the metric based on simulated and real demand time series from the automotive aftermarket.
author_names: Dominik Martin

Phil Spitzer

Niklas Kuhl
title: A New Metric for Lumpy and Intermittent Demand Forecasts: Stock keeping oriented Prediction Error Costs
venue: HICSS
year: 2020

abstract: A number of time series methods 13 month simple moving average (SMA13) single exponential smoothing (SES) Croston's method, and the SyntetosBoylan approximation (SBA) are well referenced methods in the literature on intermittent or lumpy demand forecasting. We apply these four methods to an industrial dataset involving more than 1000 stockkeeping units (SKUs) in the central warehouse of a firm operating in the professional electronics sector. Earlier studies have argued that the negative binomial distribution (NBD) satisfies both theoretical and empirical criteria for modeling intermittent demand. We have found that the NBD often does not provide a good fit. We apply an alternative approach, using a two stage distribution involving the uniform and negative binomial distributions, in modeling actual demand. We use modeling and simulation to evaluate the four methods in terms of statistical forecast accuracy and, more importantly, inventory system efficiency.
author_names: Adriano O Solis

Francesco Longo

Letizia Nicoletti

Aliaksandra Yemialyanava
title: Applying modeling and simulation to evaluate statistical accuracy and inventory control perfomance of lumpy demand forecasting methods
year: 2013

abstract: Advanced technical systems are complex machines that consist of several components. For these systems, it is important to forecasts defects and failure, which generate the demand for spare parts. Recently, Wang and Syntetos (2011) introduced a maintenance based model to forecast the intermittent and lumpy demand for spare parts. In this paper, we tested how well this model performs with aperiodic inspection intervals. We introduce a simple policy to create those aperiodic intervals. It turns out that the maintenance based model with aperiodic intervals performs as well as with periodic intervals. In most of the cases it outperforms the time series based benchmark.
author_names: T J J Bos
title: Forecasting spare parts demand for aperiodic inspection intervals
year: 2018

abstract: This study evaluates a number of methods in forecasting lumpy demand single exponential smoothing, Croston's method, the Syntetos Boylan approximation, an optimally weighted moving average, and neural networks (NN) The first three techniques are well referenced in the intermittent demand forecasting literature, while the last two are not traditionally used. We applied the methods on a time series dataset of lumpy demand. We found a simple NN model to be superior overall based on several scalefree forecast accuracy measures. Various studies have observed that demand forecasting performance with respect to standard accuracy measures may not translate into inventory systems efficiency. We simulate on the same dataset a periodic review inventory control system with forecast based order up to levels. We analyze resulting levels of on hand inventory, shortages, and fill rates, and discuss our findings and insights.
author_names: Adriano O Solis

Somnath Mukhopadhyay

Rafael S Gutierrez
title: Inventory control performance of various forecasting methods when demand is lumpy
year: 2010

abstract: In this paper we focus on forecasting for intermittent demand data. We propose a new aggregation framework for intermittent demand forecasting that performs aggregation over the demand volumes, in contrast to the standard framework that employs temporal (over time) aggregation. To achieve this we construct a transformed time series, the inverse intermittent demand series. The new algorithm is expected to work best on erratic and lumpy demand, as a result of the variance reduction of the non zero demands. The improvement in forecasting performance is empirically demonstrated through an extensive evaluation in more than 8000time series of two well researched spare parts data sets from the automotive and defence sectors. Furthermore, a simulation is performed so as to provide a stock control evaluation. The proposed framework could find popularity among practitioners given its suitability when dealing with clump sizes. As such it could be used in conjunction with existing popular forecasting methods for intermittent demand as an exception handling mechanism when certain types of demand are observed.
author_names: Fotios Petropoulos

Nikolaos Kourentzes

Konstantinos Nikolopoulos
title: Another look at estimators for intermittent demand
year: 2016

abstract: Aircraft maintenance can be divided into routine and non routine activities. Material demand associated with non routine maintenance is typically intermittent or lumpy: it has a large variance in frequency and quantity. Consequently, this type of demand is hard to predict. This paper introduces a method to collect time series datasets for aircraft non routine maintenance material demand. Non routine material consumption is linked to scheduled maintenance tasks to gain insight in demand patterns. A structural part selection of the Boeing 737NG fleet of an aviation partner has been sampled to generate various test cases. Subsequently, various forecasting methods are applied to these test cases and evaluated using various accuracy metrics. For the small time series datasets associated with non routine maintenance, exponentially weighted moving average (EMA) outperformed smoothing methods such as Croston's method (CR) and the Syntetos Boylan approximation (SBA) To validate the practical applicability of EMA for non routine maintenance material demand, the method has been applied and verified in the prediction of actual demand for a separate maintenance C check.
author_names: M Zorgdrager

Wim J C Verhagen

Richard Curran
title: An evaluation of forecasting methods for aircraft non routine maintenance material demand
year: 2014

abstract: Abstract Time series forecasting (TSF) is the task of predicting future values of a given sequence using historical data. Recently, this task has attracted the attention of researchers in the area of machine learning to address the limitations of traditional forecasting methods, which are time consuming and full of complexity. With the increasing availability of extensive amounts of historical data along with the need of performing accurate production forecasting, particularly a powerful forecasting technique infers the stochastic dependency between past and future values is highly needed. In this paper, we propose a deep learning approach capable to address the limitations of traditional forecasting approaches and show accurate predictions. The proposed approach is a deep long short term memory (DLSTM) architecture, as an extension of the traditional recurrent neural network. Genetic algorithm is applied in order to optimally configure DLSTM's optimum architecture. For evaluation purpose, two case studies from the petroleum industry domain are carried out using the production data of two actual oilfields. Toward a fair evaluation, the performance of the proposed approach is compared with several standard methods, either statistical or soft computing. Using different measurement criteria, the empirical results show that the proposed DLSTM model outperforms other standard approaches.
author_names: Alaa El Sagheer

Mostafa Kotb
title: Time series forecasting of petroleum production using deep LSTM recurrent networks
venue: Neurocomputing
year: 2019

abstract: Intermittent and fluctuating wind forces are detrimental to the grid. A multivariate model was proposed to improve the accuracy of wind power generation prediction in order to induce system operators to reduce risks. The model consists of three steps. First, the meteorological data such as wind speed are predicted by LSTM networks on the basis of traditional time series approaches. Then a method of similar time series matching with hierarchical search is proposed to highlight the main factors and save computing time. We use similar disparity as a criterion to select similar meteorological series and power data as training sets. Finally, similar data are inputted into LightGBM for modeling, training, and prediction. Industrial data of the wind power plant is examined case. The results are clearly display that the proposed method can effectively predict wind power in the next 6 hours and achieve high precision, which has certain engineering practical value.
author_names: Yukun Cao

Liai Gui
title: Multi Step wind power forecasting model Using LSTM networks, Similar Time Series and LightGBM
venue: 2018 5th International Conference on Systems and Informatics (ICSAI)
year: 2018

abstract: We present a novel approach to probabilistic time series forecasting that combines state space models with deep learning. By parametrizing a per time series linear state space model with a jointly learned recurrent neural network, our method retains desired properties of state space models such as data efficiency and interpretability, while making use of the ability to learn complex patterns from raw data offered by deep learning approaches. Our method scales gracefully from regimes where little training data is available to regimes where data from millions of time series can be leveraged to learn accurate models. We provide qualitative as well as quantitative results with the proposed method, showing that it compares favorably to the state of the art.
author_names: Syama Sundar Rangapuram

Matthias W Seeger

Jan Gasthaus

Lorenzo Stella

Bernie Wang

Tim Januschowski
title: Deep State Space Models for Time Series Forecasting
venue: NeurIPS
year: 2018