Nagaland University has entered the global quantum research spotlight with a breakthrough study that brings nature’s fractals into the quantum world.

The work, led by Biplab Pal, assistant professor of physics at the university’s School of Sciences, demonstrates how naturally occurring patterns such as snowflakes, tree branches, and neural networks can be simulated at the quantum scale.

Published in the peer-reviewed journal Physica Status Solidi – Rapid Research Letters, the research could influence India’s National Quantum Mission by broadening the materials and methods used to design next-generation quantum devices.

Fractals—repeating patterns seen in coastlines, blood vessels, and lightning strikes—have long fascinated scientists and mathematicians. This study uses those self-similar structures to model how electrons behave under a magnetic field within fractal geometries. Unlike most quantum device research that relies on crystalline materials, the work shows that non-crystalline, amorphous materials could also be engineered for quantum technologies.

“This approach is unique because it moves beyond traditional crystalline systems,” Pal said. “Our findings show that amorphous materials, guided by fractal geometries, can support the development of nanoelectronic quantum devices.”

The potential applications are wide-ranging. They include molecular fractal-based nanoelectronics, improved quantum algorithms through finer control of electron states, and harnessing the Aharonov-Bohm caging effect, which traps electrons in fractal geometries for use in quantum memory and logic devices.

University officials called the study a milestone for both Nagaland University and India’s quantum research ecosystem. “Our research shows a new pathway where naturally inspired fractal geometries can be applied in quantum systems,” vice-chancellor Jagadish K Patnaik said. “This could contribute meaningfully to the development of future quantum devices and algorithms.”

With this study, Nagaland University joins a small group of Indian institutions contributing visibly to international quantum research.

Google on Wednesday announced the Agent Payments Protocol (AP2), an open standard designed for AI agents to conduct secure and verifiable payments.

The protocol, developed with more than 60 payments and technology companies, extends Google’s existing Agent2Agent (A2A) and Model Context Protocol (MCP) frameworks.

Stavan Parikh, vice president and general manager of payments at Google, said the rise of autonomous agents requires a new foundation for trust. He added that AP2 establishes the foundation for authorization, authenticity, and accountability in agent-led transactions. 

“AP2 provides a trusted foundation to fuel a new era of AI-driven commerce. It establishes the core building blocks for secure transactions, creating clear opportunities for the industry–including networks, issuers, merchants, technology providers, and end users–to innovate on adjacent areas like seamless agent authorization and decentralized identity,” Parikh said.

Unlike traditional payment systems that assume a human directly initiates a purchase, AP2 addresses the challenges of proving intent and authority when an AI acts on a user’s behalf. The framework uses cryptographically signed digital contracts called Mandates to serve as verifiable proof of a user’s instructions. These can cover both real-time transactions, where a customer is present, and delegated tasks, such as buying concert tickets automatically under pre-approved conditions.

Rao Surapaneni, vice president and general manager of business applications platform at Google Cloud, said the protocol provides secure, compliant transactions between agents and merchants while supporting multiple payment types, from cards to stablecoins.

Google said AP2 will also support cryptocurrency payments through an extension called A2A x402, developed in partnership with Coinbase, Ethereum Foundation and MetaMask. This allows agents to handle stablecoin payments within the same framework.

Industry players expressed support for the initiative. Luke Gebb, executive vice president of Amex Digital Labs, said the rise of AI commerce makes trust and accountability more important than ever, and AP2 is intended to protect customers.

Coinbase head of engineering Erik Reppel said the inclusion of x402 showed that agent-to-agent payments aren’t just an experiment anymore and are becoming part of how developers actually build.

Adyen co-chief executive Ingo Uytdehaage said the protocol creates a “common rulebook” to ensure security and interoperability across the payments ecosystem.

Backers include Mastercard, PayPal, Revolut, Salesforce, Worldpay, Accenture, Adobe, Deloitte and Dell, who said the framework could open up opportunities for secure agent-driven commerce ranging from consumer shopping to enterprise procurement.

Google has published the technical specifications and reference implementations in a public GitHub repository and invited the wider payments and technology community to contribute to its development.

Image by Editor | ChatGPT

# Introduction

 
Time series forecasting is everywhere in business. Whether you’re predicting sales for next quarter, estimating inventory demand, or planning financial budgets, accurate forecasts can make — or break — strategic decisions.

However, classical time series approaches — like painstaking ARIMA tuning — are complicated and time-consuming.

This presents a dilemma for many data scientists, analysts, and BI professionals: precision versus practicality.

That’s where a lazy data scientist’s mindset comes in. Why spend weeks fine-tuning models when modern Python forecasting libraries and AutoML can give you an adequate solution in less than a minute?

In this guide, you’ll learn how to adopt an automated forecasting approach that delivers fast, reasonable accuracy — without guilt.

# What Is Time Series Forecasting?

 
Time series forecasting refers to the process of predicting future values derived from a sequence of historical data. Common applications include sales, energy demand, finance, and weather, among others.

Four key concepts drive time series:

• Trend: the long-term tendency, shown by increases or decreases over an extended period.
• Seasonality: patterns that repeat regularly within a year (daily, weekly, monthly) and are associated with the calendar.
• Cyclical: repeating movements or oscillations lasting more than a year, often driven by macroeconomic conditions.
• Irregular or noise: random fluctuations we cannot explain.

To further understand time series, see this Guide to Time Series with Pandas.

Image by Author

# The Lazy Approach to Forecasting

 
The “lazy” approach is simple: stop reinventing the wheel. Instead, rely on automation and pre-built models to save time.

This approach prioritizes speed and practicality over perfect fine-tuning. Consider it like using Google Maps: you arrive at the destination without worrying about how the system calculates every road and traffic condition.

# Essential Tools for Lazy Forecasting

 
Now that we have established what the lazy approach looks like, let’s put it into practice. Rather than developing models from the ground up, you can leverage well-tested Python libraries and AutoML frameworks that will do most of the work for you.

Some libraries, like Prophet and Auto ARIMA, are great for plug-and-play forecasting with very little tuning, while others, like sktime and Darts, provide an ecosystem with great versatility where you can do everything from classical statistics to deep learning.

Let’s break them down:

// Facebook Prophet

Prophet is a plug-and-play library created by Facebook (Meta) that’s especially good at capturing trends and seasonality in business data. With just a few lines of code, you can produce forecasts that include uncertainty intervals, with no heavy parameter tuning required.

Here is a sample code snippet:

from prophet import Prophet
import pandas as pd

# Load data (columns: ds = date, y = value)
df = pd.read_csv("sales.csv", parse_dates=["ds"])

# Fit a simple Prophet model
model = Prophet()
model.fit(df)

# Make future predictions
future = model.make_future_dataframe(periods=30)
forecast = model.predict(future)

# Plot forecast
model.plot(forecast)

// Auto ARIMA (pmdarima)

ARIMA models are a traditional approach for time-series predictions; however, tuning their parameters (p, d, q) takes time. Auto ARIMA in the pmdarima library automates this selection, so you can obtain a reliable baseline forecast without guesswork.

Here is some code to get started:

import pmdarima as pm
import pandas as pd

# Load time series (single column with values)
df = pd.read_csv("sales.csv")
y = df["y"]

# Fit Auto ARIMA (monthly seasonality example)
model = pm.auto_arima(y, seasonal=True, m=12)

# Forecast next 30 steps
forecast = model.predict(n_periods=30)
print(forecast)

// Sktime and Darts

If you want to go beyond classical methods, libraries like sktime and Darts give you a playground to test dozens of models: from simple ARIMA to advanced deep learning forecasters.

They’re great for experimenting with machine learning for time series without needing to code everything from scratch.

Here is a simple code example to get started:

from darts.datasets import AirPassengersDataset
from darts.models import ExponentialSmoothing

# Load example dataset
series = AirPassengersDataset().load()

# Fit a simple model
model = ExponentialSmoothing()
model.fit(series)

# Forecast 12 future values
forecast = model.predict(12)
series.plot(label="actual")
forecast.plot(label="forecast")

// AutoML Platforms (H2O, AutoGluon, Azure AutoML)

In an enterprise environment, there are moments when you simply want forecasts without having to code and with as much automation as possible.

AutoML platforms like H2O AutoML, AutoGluon, or Azure AutoML can ingest raw time series data, test several models, and deliver the best-performing model.

Here is a quick example using AutoGluon:

from autogluon.timeseries import TimeSeriesPredictor
import pandas as pd

# Load dataset (must include columns: item_id, timestamp, target)
train_data = pd.read_csv("sales_multiseries.csv")

# Fit AutoGluon Time Series Predictor
predictor = TimeSeriesPredictor(
    prediction_length=12, 
    path="autogluon_forecasts"
).fit(train_data)

# Generate forecasts for the same series
forecasts = predictor.predict(train_data)
print(forecasts)

# When “Lazy” Isn’t Enough

 
Automated forecasting works very well most of the time. However, you should always keep in mind:

• Domain complexity: when you have promotions, holidays, or pricing changes, you may need custom features.
• Unusual circumstances: pandemics, supply chain shocks, and other rare events.
• Mission-critical accuracy: for high-stakes scenarios (finance, healthcare, etc.), you will want to be fastidious.

“Lazy” does not mean careless. Always sanity-check your predictions before using them in business decisions.

# Best Practices for Lazy Forecasting

 
Even if you’re taking the lazy way out, follow these tips:

1. Always visualize forecasts and confidence intervals.
2. Compare against simple baselines (last value, moving average).
3. Automate retraining with pipelines (Airflow, Prefect).
4. Save models and reports to ensure reproducibility.

# Wrapping Up

 
Time series forecasting does not need to be scary — or exhaustive.

You can get accurate, interpretable forecasts in minutes with Python forecasting libraries like Prophet or Auto ARIMA, as well as AutoML frameworks.

So remember: being a “lazy” data scientist does not mean you are careless; it means you are being efficient.
 
 

Josep Ferrer is an analytics engineer from Barcelona. He graduated in physics engineering and is currently working in the data science field applied to human mobility. He is a part-time content creator focused on data science and technology. Josep writes on all things AI, covering the application of the ongoing explosion in the field.