README.md

metadata

license: cc-by-nd-4.0

Time-Series Donations Dataset

Overview

This repository provides a time-series dataset of donation dynamics over time.
It is intended for experiments in:

• Time-series forecasting
• Trend and seasonality analysis
• Anomaly detection on donation flows
• Benchmarking classical and deep time-series models

The data are organized in a tabular time-series format, with each row representing a time step and each column representing a numerical or categorical feature related to donations.

Repository Contents

• time series adv-donations.xlsx
  Main time-series file containing the donation-related data.

(If additional files are later added—e.g., CSV exports or documentation—they can be listed here.)

Data Schema (example)

Columns may include (adjust to your actual header names):

• date or timestamp: time index of the observation
• donation_amount: total donated amount in the given time window
• donation_count: number of donations in the given time window
• campaign_id or channel: optional categorical identifiers
• Other engineered or contextual features (e.g., weekday, holiday flags, etc.)

How to Use

This dataset is suitable for:

• Building forecasting models (ARIMA, Prophet, LSTM, TCN, Transformers, etc.)
• Comparing different time-series pipelines
• Exploring seasonality, trends, and external influences on donation behavior
• Teaching and experimentation in time-series modeling

No personal or directly identifying donor information is included in this dataset.

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Loading the Dataset from Hugging Face

Below are example snippets showing how to download and load the dataset directly from Hugging Face.

1. Using huggingface_hub + pandas (recommended for Excel)

from datasets import load_dataset

# Carica il dataset dal repo Hugging Face (sostituisci con il tuo repo_id)
ds = load_dataset("VillanovaAI/Time-Series-Donations")

# ds è un DatasetDict; si assume che il file CSV/Parquet generi lo split “train”
df = ds["train"].to_pandas()  # converte in pandas DataFrame

print(df.head())