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GP-DA-1NYBF62Data and AIOpen for request

Indian tourism footfall forecasting

A time-series comparison system for forecasting visitor footfall and explaining seasonal patterns at selected Indian destinations using official aggregate data.

  • Nuxt
  • Vue
  • FastAPI
  • PostgreSQL
  • Python
  • Pandas
  • scikit-learn
  • Docker

Project definition

Problem statement

Visitor footfall changes with seasons, holidays, destination type, reporting frequency, and unusual events. Annual totals alone do not show short-term patterns, while limited historical records can make complex models unreliable.

The engineering problem is to prepare consistent aggregate time series, identify seasonal structure, compare forecasting methods honestly, and show uncertainty and data limitations with every result.

Project objectives

  • Prepare aggregate visitor series for selected destinations, states, or monuments from official sources.
  • Analyse trend, seasonality, holidays, gaps, and structural changes in each series.
  • Compare naive, seasonal, statistical, and selected machine-learning forecasts.
  • Use rolling backtests to measure performance at multiple forecast horizons.
  • Present forecasts, uncertainty, and historical accuracy in a readable dashboard.

System design

System modules

01

Tourism data importer

Loads approved aggregate visitor records and preserves source, geography, visitor category, period, unit, and release date.

02

Series validator

Checks frequency, missing periods, duplicates, revisions, category changes, and unusual values before modelling.

03

Seasonal analyser

Calculates trend and seasonal components and relates prepared calendar features to recurring patterns.

04

Forecast engine

Runs naive, seasonal, statistical, and selected machine-learning models through a common interface.

05

Backtesting module

Compares errors and interval coverage across rolling dates, destinations, and horizons.

06

Destination dashboard

Shows source data, seasonality, forecasts, uncertainty bands, model history, and downloadable summaries.

Methodology

System workflow

  1. 01
    Select destination

    The user selects a prepared destination or region, visitor category, time frequency, and forecast horizon.

  2. 02
    Validate series

    The system displays coverage, gaps, revisions, unusual periods, and the transformations used.

  3. 03
    Analyse pattern

    Trend, seasonal components, and prepared calendar features are calculated from past observations only.

  4. 04
    Run backtests

    Selected models forecast from historical cut-off dates and are compared against naive baselines.

  5. 05
    Display forecast

    The best documented result is shown with uncertainty, accuracy history, source date, and limitations.

Demonstration scenario

A user selects a prepared destination with monthly visitor data. The dashboard displays the trend and recurring seasonal peak, then compares a seasonal baseline with two forecast models through rolling backtests. The selected model produces the next six months with an uncertainty band and its previous accuracy.

Engineering

Technical architecture

Web application
Nuxt and Vue for destination selection, historical charts, seasonal views, model comparison, and forecast export.
Forecast API
FastAPI for prepared series, decomposition, model execution, backtests, forecasts, and experiment records.
Data layer
PostgreSQL for official source records, cleaned series, calendar features, model versions, backtests, and forecasts.
Modelling pipeline
Python, Pandas, statistical models, and scikit-learn with time-aware transformations and replaceable model adapters.
Evaluation
Rolling-origin comparisons, naive baselines, horizon-level errors, prediction intervals, and structural-break notes.
Delivery
Docker-based setup with prepared destination series, saved model configurations, and reproducible evaluation commands.

Testing

Evaluation

Evaluation measures

  • Mean absolute error, root mean squared error, and symmetric mean absolute percentage error
  • Performance relative to naive and seasonal-naive forecasts
  • Prediction-interval coverage and average interval width
  • Accuracy across destinations, seasons, and forecast horizons
  • Sensitivity to missing periods and unusual event years
  • Data preparation and forecast execution time

System boundaries

  • The prototype uses aggregate public statistics and does not track individual visitors.
  • Forecasts cover only the selected destinations, visitor categories, and published time periods.
  • Unexpected policy, weather, transport, health, or social events may not be represented by historical patterns.
  • The forecasts support analysis and do not replace operational planning by tourism authorities.

Included

  1. 01Destination history and seasonal-analysis dashboard
  2. 02Data preparation, forecasting, and backtesting pipelines
  3. 03Model comparison and forecast-uncertainty visualisations
  4. 04Prepared official aggregate data, experiments, and results
  5. 05Complete source code in a private GitHub repository
  6. 06Complete project documentation with synopsis, abstract, methodology, data-flow diagrams, forecast results, screenshots, and conclusion
  7. 07Setup and usage guide

Project record

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Permanent project ID
GP-DA-1NYBF62
Catalogued
21 Aug 2026
Completed
Pending
Verified
Pending
Demonstration
Added when ready

Handover

After purchase

  1. 01
    Payment is confirmed

    The project is marked unavailable and cannot be purchased again.

  2. 02
    Repository access is granted

    The buyer's submitted GitHub account receives access to the private repository.

  3. 03
    The purchase record is delivered

    The certification sheet is prepared from the reviewed buyer details and sent privately by email.