This is my first ML project ever.

Well the objective is to build a forecasting model of a univariate time series containing solid waste weights loaded from the city of Austin,Texas.

The distribution of the data is shown below. enter image description here First, I want to fit an ARIMA model to the dataset. But it cannot capture well the values since they are following a multimodal distribution. So I tried to segment it on two datasets: one dataset for the weekends and an other one for business days so that I would have to fit two forecasting models for each. However, the segmentation criterion does not work and business days dataset is also bimodal.

I also tried to proceed with kmeans clustering by creating two clusters. Howerver, one of the two clusters is still multimodal.

Can anyone give me suggestions?

  • $\begingroup$ Show us your dataset URL, please. Apparently some days there's approximately zero sanitation workers scheduled to make a run, and other days we expect almost 2 million grams (kg? tons? slugs?) of solid waste. The density estimate seems to be using a Gaussian kernel with infinite support, which is an imperfect fit for the Business Domain when we look to the left of the origin, since there's no days where we make a pickup at the landfill and distribute material to downtown locations. $\endgroup$
    – J_H
    Commented Mar 24 at 17:07
  • $\begingroup$ kaggle.com/datasets/ivantha/… $\endgroup$ Commented Mar 24 at 17:24
  • $\begingroup$ The weight units are in Kg $\endgroup$ Commented Mar 24 at 17:24
  • $\begingroup$ I will channel what your professor is continually telling y'all: "Label your charts!" Folks should be able to interpret the meaning of any given chart without needing to resort to flipping through paragraphs of its associated paper or going back to ask the author. $\endgroup$
    – J_H
    Commented Mar 24 at 17:26

1 Answer 1


Consider simplifying seasonality by reducing to just 7-day totals.

You have encountered a very common modeling situation where there are multiple Generating Processes out in the real world (such as discarding gift wrapping in December) which all contribute with varying magnitudes to your target variable Y. You will want to identify a subset of them, rank order their magnitudes, and start modeling them one by one, in an effort to explain more and more variance.

Suppose that is_working_day has the greatest predictive power, explaining most of the variance. Then you will want to synthesize such a column from your raw yyyy-mm-dd datestamps, and let random forests, or some other modeling technique, have access to it when inferring future forecasts. Notice that you'll have to augment the initial dataset with things like "bank holidays", "federal holidays", "sanitation worker holidays", which may overlap while being non-identical.

Now you have residuals, the delta between something observed in your input data and its corresponding model prediction. Train a new model on the residuals, perhaps based on day_number or daily_high_celsius to predict seasonality over the course of a year. You will have new residuals, which will be smaller if your more complex model has greater predictive power.

At this point, having de-trended the weekly and annual seasonality, you may be ready to account for year-over-year population growth with an ARIMA model on the residuals.

Alternatively, you may prefer to gather apparently relevant features, including census population, highway traffic volumes, and tax revenue, up front. And then feed them all at once to e.g. XGBoost, letting the model figure out which ones are truly informative.

You may find that the following (non-ARIMA) model adequately captures the dynamics of interest.

#! /usr/bin/env python

from pathlib import Path

from pandas.tseries.holiday import USFederalHolidayCalendar
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.model_selection import train_test_split
import pandas as pd

CSV = Path("open_source_austin_daily_waste_2003_jan_2021_jul.csv")

def get_garbage_df() -> pd.DataFrame:
    df = pd.read_csv(CSV)
    df["ticket_date"] = pd.to_datetime(df.ticket_date)
    df["elapsed"] = (df.ticket_date - df.ticket_date.min()).dt.days
    df["month"] = df.ticket_date.dt.month

    holidays = USFederalHolidayCalendar().holidays(
    df["holiday"] = df.ticket_date.isin(holidays)
    df["weekday"] = df.ticket_date.dt.weekday
    return df

def main() -> None:
    df = get_garbage_df()
    y = df.net_weight_kg
    df = df.drop(columns=["net_weight_kg", "ticket_date"])
    X = df
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
    reg = GradientBoostingRegressor(random_state=0)
    reg.fit(X_train, y_train)
    print(reg.score(X_test, y_test))

if __name__ == "__main__":

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