Ren yirenlu92

Forecasting timeseries with the Metered Prophet API

Forecasting timeseries

Forecasting timeseries is a common problem in data science/machine learning. It asks, given a set of observations of the past, what the future will look like.

Some real world applications of timeseries forecasting include:

Sales/demand forecasting: Say you're an ice cream chain. You might expect that sales will be much higher in the summer and lower in the winter, but trend higher year-over-year overall because you're investing in advertising. The sales forecasts would be useful for things like setting quota for your salespeople, financial disclosure/valuation, and inventory planning.
Capacity planning: In a software context, capacity planning refers to ensuring enough compute resources to serve expected traffic. More broadly, capacity planning asks, how many servers, employees, meals, parking spaces, etc

	import os
	import sys
	from dataclasses import dataclass
	from pathlib import Path

	from fastapi import FastAPI
	from modal import Image, App, Volume, gpu, Secret, enter, method, asgi_app

	GIT_SHA = "abd922bd0c43a504e47eca2ed354c3634bd00834" # specify the commit to fetch

	import openai
	import json
	import snowflake.connector

	table_metadata = """
	Table name: agents
	name type kind null? default primary key unique key check expression comment policy name
	AGENT_CODE VARCHAR(6) COLUMN N Y N
	AGENT_NAME VARCHAR(40) COLUMN Y N N
	WORKING_AREA VARCHAR(35) COLUMN Y N N

	from flask import Flask, request

	app = Flask(__name__)

	@app.route('/basic_api/entities', methods=['GET', 'POST'])
	def entities():
	if request.method == "GET":
	return {
	'message': 'This endpoint should return a list of entities',
	'method': request.method

	from typing import Optional

	from fastapi import FastAPI
	from pydantic import BaseModel


	class Entity(BaseModel):
	name: str
	description: Optional[str] = None