From d5642357be8f3f475fa41a7af0d9367704ce19b1 Mon Sep 17 00:00:00 2001
From: baggiponte <57922983+baggiponte@users.noreply.github.com>
Date: Wed, 6 Dec 2023 00:46:18 +0100
Subject: [PATCH] refactor: update introduction
---
slides.md | 46 ++++++++++++++++++++++++++--------------------
1 file changed, 26 insertions(+), 20 deletions(-)
diff --git a/slides.md b/slides.md
index 4343b2a..846ac7f 100644
--- a/slides.md
+++ b/slides.md
@@ -16,7 +16,6 @@ a **next-generation ML forecasting library**
👤 Luca Baggi
💼 ML Engineer @xtream
🛠️ Maintainer @functime
-
@@ -32,7 +31,7 @@ a **next-generation ML forecasting library**
### 🔮 The problem with forecasting
-### 🐻❄️ What is Polars?
+### 📈 `functime`'s answer
### 🚀 Why is Polars so fast?
@@ -52,41 +51,48 @@ a **next-generation ML forecasting library**
# 🔮 The problem with forecasting
A new paradigm to evaluate the forecasting process
-
-
-*We spend far too many resources generating, reviewing, adjusting, and approving our forecasts, while almost **invariably failing to achieve the level of accuracy desired**. The evidence now shows that a large proportion of typical business forecasting efforts fail to improve the forecast, or even make it worse. So the conversation needs to change. The focus needs to change.*
+*"We spend far too many resources generating, reviewing, adjusting, and approving our forecasts, while almost **invariably failing to achieve the level of accuracy desired**."* (source)
-*We need to **shift our attention from esoteric model building to the forecasting process itself – its efficiency and its effectiveness**.*
+
-
-Mike Gilliland
-
+Mike Gilliland
Board of Directors of the International Institute of Forecasters
-
-
-
+
---
# 🔮 The problem with forecasting
+A new paradigm to evaluate the forecasting process
+
+*"The focus needs to change. We need to shift our attention **from esoteric model building to the forecasting process itself** – its **efficiency** and its **effectiveness**."* (source)
+
+
+
+Mike Gilliland
+Board of Directors of the International Institute of Forecasters
+
+
+
+---
+
+# 📈 `functime`'s answer
Reframe the problem
-Make forecasting just work at a **reasonable scale** (~90% of use cases):
+Make forecasting just work at a **reasonable scale** (~90% of use cases).
+
+
1. Forecast **thousands of time series** without distributed systems (PySpark).
-2. Provide adequate **diagnostic tools**.
-2. Smoothly translate form experimentation to production.
+2. **Feature-engineering** and **diagnostics** API compatible with panel datasets.
+3. Smoothly translate form experimentation to production.
+
-🫢 Spoiler alert: we used Polars and global forecasting
-
+This can be achieved with two ingredients: **Polars** and **global forecasting**.
-
---