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English
$
USD
I will perform time series analysis and forecasting
Data Scientist
I am a Full Stack Developer and Data Scientist with a background in research and statistical analysis. I help students, researchers, and businesses make sense of their data — from hypothesis testing a...
About this Gig
Need to predict next month's sales? Plan inventory for the next quarter? Understand seasonal patterns in your data? I'll build a reliable forecasting model that captures trends, seasonality, and cyclical behavior in your historical data.
What I work with:
- Stationarity testing: ADF and KPSS tests
- Time series decomposition: trend, seasonality, residual
- ARIMA, SARIMA, SARIMAX modeling
- Facebook Prophet for robust seasonal forecasting
- Model selection using AIC, BIC, MAPE, and RMSE
- Forecast output with confidence intervals
What you'll receive:
- Jupyter Notebook with complete analysis and forecasting pipeline
- Forecast output file with confidence intervals (CSV)
- Visualizations: decomposition plots, actual vs predicted, forecast chart
- Summary report with interpretation and business recommendations
To get started, I'll need:
- Your time series dataset in CSV or Excel format
- Data frequency: daily, weekly, monthly, etc.
- Your forecast horizon: how many periods ahead you need
Not included: real-time data feeds, automated retraining pipelines, deployment or production setup.
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FAQ
What data do I need to provide?
A CSV or Excel file with at least two columns: a date/time column and the numeric variable you want to forecast (sales, demand, traffic, etc.). The longer the historical record, the more reliable the forecast.
How much historical data do I need?
As a minimum, I recommend at least 24 data points for monthly data (2 years), or equivalent for other frequencies. More data generally means better forecast accuracy, especially for capturing seasonality.
How far ahead can you forecast?
The Standard package covers up to 30 periods ahead, and the Premium package up to 90 periods. For very long horizons, forecast uncertainty increases — I'll always include confidence intervals so you know the range of likely outcomes.
Which model will you use — ARIMA or Prophet?
It depends on your data characteristics. ARIMA/SARIMA works well for stationary or trend-driven series. Prophet handles multiple seasonalities and missing data more robustly. The Premium package includes a comparison of both so you can see which fits your data better.
My data has gaps and irregular dates. Is that a problem?
Gaps and irregularities can usually be handled through interpolation or resampling. Let me know the nature of the gaps when you order and I'll advise accordingly.
Can you forecast multiple products or locations at once?
The standard scope covers one time series per order. For multiple series, message me before ordering so we can discuss a custom arrangement.
