Sliding Window Price Predictions

Sliding Window Price Predictions

The code used here is available in its original repository in .ipynb format. You can download it & fiddle with it in Jupyter Notebooks on…After spending a year or so on the self-taught path to programming with Python, I hit the reset button and started over by taking a course in _Intensive Program Design_. After just a few weeks, I picked up a load of important lessons in the fundamentals of writing software that I never cared to learn before. In a way, I finally learned enough to learn _how to learn to program_, which is a skill that I did not know I needed. In this story, I memorialize part of what I’ve learned so far, partly so I don’t forget, but also, to share a few tips on how to understand abstraction and why it is important. ## Lesson 1 — You already know abstractions, no sweat Ever use a built-in function like **sum()** to add a list of numbers or **len()** to get the length of an object in Python? If so, you already know what an abstraction is, that is, a function that hides how it does what it does so you can get on with your life. For instance, with a simple example below, we can see how **sum()** hides _how_ it adds a list of numbers by manually creating a loop that does the same job. ``` ## how to add a list of numbers, two ways lst_of_numbers = [2,3,5] ## example of using a built-in function as an abstraction sum_with_abstraction = sum(lst_of_numbers) print('With Built-in Function:', sum_with_abstraction) ## example of getting sum without a built-in function sum_with_loop = 0 for i in lst_of_numbers: sum_with_loop += i print('Without Built-in Function:', sum_with_loop) ## Output ## >>> With Built-in Function: 10 ## >>> Without Built-in Function: 10 ``` ## Lesson 1 Conclusion: So what’s the big deal about recognizing built-in functions as a form of abstraction? Most importantly— **do not be intimidated when someone throws the word _abstraction_ around because you already know what it is.** Further, it’s worth recognizing a built-in function exists for most of your programming needs and probably performs better than building something from scratch. However, what to do about other tasks without a ready-to-go function?


Today we’ll be seeing how we can use historic produce prices to make predictions over a twenty year period. This will be done in Python using a simple linear regression model. Beautiful Soup 4 helps with parsing the observations from an online source. This data will then be accessed & manipulated from a Pandas dataframe. Training will be done on a sliding window; this and model fitting will be conducted using** Sci Kit Learn**.

Our package imports:

from bs4 import BeautifulSoup
import requests
import numpy as np
import pandas as pd
from pandas import concat
from sklearn.model_selection import TimeSeriesSplit
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from math import sqrt

This post covers the core functions for linear regression using a sliding window on time series data. To see more on visualizing the results of our linear regression with *Yellowbrick *as well as other findings, please see original repository.

✧ Outline of this Post:

  1. Preparation — Getting model ready data with acquisition, cleaning & feature engineering
  2. _Model Fitting _— Linearly regressing time series splits
  3. _About the Data _— More on produce


The function created for acquiring Produce data is very much tailored for the unique website: Produce Price Index. Here’s a look at the sort of information we’ll be extracting.

python timeseries-forecasting web-scraping linear-regression

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