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<p>Dear all,</p>
<p>On Thursday (21 March) at 2pm in <span class="event-where">54 / 7035 (7B), we have an S3RI seminar from
</span>Ioanna Manolopoulou<span class="event-where"> (UCL) on </span><span class="event-where"><span class="event-description">"</span></span><span class="event-where"><span class="event-description"><span class="event-description"><span class="event-description">Bayesian
hierarchical modelling of sparse count processes with applications in retail analytics</span></span>". Details are given below.<br>
</span></span></p>
<p><span class="event-where"><span class="event-description"><span><span class="event-description">The seminar will also be available via a live web-cast at</span></span></span></span><br>
<a class="moz-txt-link-freetext" href="https://coursecast.soton.ac.uk/Panopto/Pages/Viewer.aspx?id=b75145d0-d79a-44d3-8ae6-aa140085e165">https://coursecast.soton.ac.uk/Panopto/Pages/Viewer.aspx?id=b75145d0-d79a-44d3-8ae6-aa140085e165</a><br>
</p>
<p><span class="event-where"><span class="event-description"><span><span class="event-description">The talk will be followed by tea and cake in the staff reading room on level 4 of building 54. </span></span></span></span></p>
<p><span class="event-where"><span class="event-description"><span><span class="event-description">All are welcome!</span></span></span></span></p>
<p><span class="event-where"><span class="event-description"><span>Best wishes,</span></span></span></p>
<p><span class="event-where"><span class="event-description"><span>Helen</span></span></span></p>
<p><span class="event-details-label"></span><span class="event-description">Bayesian hierarchical modelling of sparse count processes with applications in retail analytics
<br>
</span></p>
<p><span class="event-description">Ioanna Manolopoulou, University College London
<br>
</span></p>
<p><span class="event-description">In retail analytics, slow-moving-inv<wbr>entory (SMI) refers to goods which rarely sell, resulting in very sparse count processes. Forecasting the sales of such goods is challenging, because traditional predictive models rely
on large enough sales volumes to be accurate. In this work, we develop modelling, inferential and predictive methods able to learn the dynamics of sparse count processes for SMI products with few to no sales. We flexibly introduce covariates into the self-exciting
model for sparse processes of Porter et al., (2012). We extend the model to include a cross-excitatio<wbr>n contribution that allows differing series to excite one another, capturing the process of intertwined contemporaneous excitation dynamics. We integrate
individual products into a Bayesian hierarchical model that accommodates shrinkage and information passing across differing sparse count process, without requiring the data for each product to exist over the same time period. We illustrate our methods on a
retail analytics dataset from a major supermarket chain in the UK. <br>
</span></p>
<p><span class="event-description">Joint work with James Pitkin and Gordon Ross</span></p>
<br>
<span class="event-where"><span class="event-description"><span><span class="event-description"><span><span class="event-description"></span><span class="event-description"><span class="event-description">For the current schedule of S3RI seminars, see
<a class="moz-txt-link-freetext" href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Ftinyurl.com%2Fs3riseminar&data=01%7C01%7C%7C193afa03bb184d1b85b208d6ab791422%7C4a5378f929f44d3ebe89669d03ada9d8%7C0&sdata=8a9uwWjBqipYbpmCcB%2BzbOAUkt9MDS4iZKlM6KsT1rg%3D&reserved=0" originalsrc="https://tinyurl.com/s3riseminar" shash="W+UHYyjXeTOojBkptA7/Y2V3Zo5A48OoUjX+MAQgsrP12zckI9VC96omP0Id7c88Fx6BlSQnvd7KOVZULx9ZNV1BIiPkVNvEfW9KglQlseyU2YA4cZNWqqAIWRPr4Z5WExxyQFUSk4Y9bw8AoDQfJqiVcVEanIgr1WXDyhJWBck=">
https://tinyurl.com/s3riseminar</a> </span></span></span></span></span></span></span><br>
<p><span class="event-where"><span class="event-description"><span><span class="event-description"></span></span></span></span></p>
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