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Forecasting-Time series regression

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Intelligencia.co

Forecasting-Time series regression

Time series regression is a statistical method for predicting a future response based on the response history (known as autoregressive dynamics) and the transfer of dynamics from relevant predictors. Time series regression can help a business understand and predict the behavior of dynamic systems from experimental or observational data. Time series regression is commonly used for modeling and forecasting of economic, financial, and biological systems.

Forecasting-Time series regression work that we have done in the past includes:

  • Time Series models – delivered for The Department of Health and Ageing (Australia) for forecasting national script counts as part of a major project within the Department. Nearly 100 different models were built at a drug type level that could forecast script counts five years in advance. Data set used was 10 years of national script data and these contained over 1.8 billion records. The results revolutionized the Department’s approach to negotiations regarding the PBS scheme. Anecdotal evidence suggests the savings alone from the forecast models and new arrangement were well over AUD $2 billion and allowed in part for current health reform to take place with minimal costs to the taxpayer.
  • Time Series Regression – models were built for the Australian public hospital activity, as part of the move to Activity Based Funding under the National Health Reform program. Separate models were needed for admitted patients, non-admitted patients and outpatients. The Department also required analysis on the effect of environmental changes on hospital utilisation within the Health system for scenario modeling. Such effects included average length of stay, hospital size (staff and beds) and population characteristics. Initial modeling and results helped set the initial price for each and every separation/procedure/operation.
  • Forecasting – a major electronics retailer required models, including exogenous variables that would forecast SKUs at the store level. The nature of the electronics business, and the short shelflife of most products presented many and varied challenges. Final models helped with improvements to the logistic and supply chain process as stock could be better managed. The inclusion of exogenous variables also provided category managers with previously unavailable insight around price elasticity, marketing effectiveness and calendar effects.
  • Time Series Regression Model constructed for a major Australian retailer of fast moving consumer goods, these models revealed the price elasticity of multiple SKUs. Resultant output enabled the retailer to understand the effect of price increases and decreases on sales volume.
  • Commodity Forecasting Models – developed for a multinational construction company, these models gave the business better pricing power. Due to the nature of the construction tender process, multiple years elapse between initial tender submission and the first till of the soil. A lack of understanding of commodity prices saw the companies strikerate for correctly pricing tenders linger around the one-in-two mark, an almost unacceptably low hit-rate. Steel, glass, cement, sand and timber were all investigated and crossed with multiple economic indicators to derive robust forward estimates of prices for a 24-month window.
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