It is challenging to build useful forecasts for sparse demand products. If the forecast is lower than the actual demand, it can lead to poor assortment and replenishment decisions, and customers will not be able to get the products they want when they need them. If the forecast is higher than the actual demand, the unsold products will occupy inventory shelves, and if the products are perishable, they will have to be liquidated at low costs to prevent spoilage. The overall objective of the model is to use the retail data which provides us with historic sales across various countries and products for a firm. We use this information given, and make use of FM’ s to predict the sparse demand with missing transactions. The above step then enhances the overall demand forecast achieved with LSTM analysis. As part of the this project we answered the following questions: How well does matrix factorization perform at predicting intermittent demand How does matrix factorization approach improve the overall time-series forecasting

Texas Wind - Turbine About Dataset Problem Statement: The intermittent nature and low control over the wind conditions bring up the same problem to every grid operator in their successful integration to satisfy current demand. In combination with having to predict demand and balance it with the supply, the grid operator now also must predict the availability of wind and solar generation plants in the next hour, day, or week. Apart from holding back the benefits of renewable energy, incorrectly scheduling of wind generation plants may lead to unnecessary reservations, higher costs passed over to the consumer, and use of other more expensive and polluting power resources. Working with real data is challenging due to noise and missing periods. Dataset details: The provided full-year hourly time-series are simulated using the National Renewable Energy Laboratory (NREL) software for a location in Texas, US. It has perfect data completeness, and no noisy data; challenges that hinder forecasting tasks with real datasets and distract from the goal. The dataset contains various weather features which can be analyzed and used as predictors. Colums: Time stamp System power generated | (kW) Wind speed | (m/s) Wind direction | (deg) Pressure | (atm) Air temperature | ('C) Work plan 🤝🤝🤝🤝🤝 1- Data Exploration & Analysis 🤝🤝🤝 2- Building a Machine Learning Model / Predict

How To Apply Time Embeddings To A Classification and Quantity Forecast In Tensorflow Embedding time along with categorical and continuous features offsets the errors caused by intermittent time series. This event often occurs in manufacturing or retail businesses that distribute through multiple overlapping distributors or stores and regions with a large parts list. The solution in tensorflow is demonstrated for an international specialty fastener manufacturer with over 2000 part items and 200 distributors in 5 regions. The manufacturer needed pricing support at the quote level by part, distributor and region to project whether or not a quote would become an order and the expected quantity to be sold during a successful order so that it could align with their current demand planning methods. The first session half discusses applications and solutions while the session second half explains the feature development and the non-linear tensorflow model in depth. The annotated open source code in a jupyter notebook is provided for reference.

:exclamation: This is a read-only mirror of the CRAN R package repository. tsintermittent — Intermittent Time Series Forecasting. Homepage: https://kourentzes.com/forecasting/2014/06/23/intermittent-demand-forecasting-package-for-r/

No description available

Forecasting Intermittent Time Series

No description available

This repository is dedicated to forecasting intermittent demand in the pharmaceutical industry. It leverages advanced time-series forecasting techniques, robust data preprocessing, and scalable model evaluation pipelines to predict demand patterns for various pharmaceutical products.

Photovoltaic power generation has always been a research hotspot in the field of new energy. However, as the demand for energy efficiency has increased, the sustainable development of the photovoltaic industry has encountered great challenge. The power grid has refused to connect large-scale photovoltaic power generation into the grid, and the phenomenon of “abandoning light” has become increasingly serious. These force us to carry out accurate PV forecasting, adopt more accurate scheduling decisions, and achieve multi-energy coordinated control, so that intermittent power supply grids have technical support. Traditional PV forecasting methods have gradually lost their advantages in the face of increasing PV data. The physical method requires an accurate prediction model and a large number of empirical coefficients. The process is cumbersome and the error is large. The statistical method requires a large amount of historical operational data, which has high requirements for the optimization of the computational model. In view of this, this paper adopts a framework based on deep learning; considering that the influence factors of PV prediction are physical quantities that change with time, and the recurrent neural network (RNN) algorithm has strong processing ability for time series; The feature set constitutes a training set, and the most important structure in RNN——long-short-term memory network (LSTM), is used to predict photovoltaic power generation. LSTM has the functions of “forget” and “update”, which solves the problem of long-order dependency, so that the feature can be well preserved in subsequent calculations without memory dissipation. The results show that the PV prediction based on the LSTM model has a qualitative change in the prediction accuracy, and the prediction speed is also significantly improved.

No description available

Forecasting using DL on intermittent time series

Code repository for "Adversarial Learning for Intermittent and Lumpy Time-Series Forecasting"

Unified framework for evaluating time-series demand forecasting models across stable, fast, and intermittent patterns using statistical, ML, and AutoML approaches with rolling validation and MASE.

An experimentation project using the M5 Forecasting competition dataset. Building an end-to-end ML pipeline to predict 28 days of Walmart sales across 30,490 products. Currently focused on optimizing data processing and testing various feature engineering strategies for hierarchical, intermittent time-series data.

Intermittent demand-when a product or SKU experiences several periods of zero demand-is highly variable. Intermittent demand is very common in industries such as aviation, automotive, defense, manufacturing, and retail. It also typically occurs with products nearing the end of their lifecycle. # However, due to the many zero values in intermittent demand time series, the usual methods of forecasting, such as exponential smoothing and ARIMA, do not give an accurate forecast. In these cases, approaches such as Croston may provide a better accuracy over traditional methods.