Key Facts
- Category
- Data & Tables
- Input Types
- textarea, file, text, select, number
- Output Type
- html
- Sample Coverage
- 4
- API Ready
- Yes
Overview
The Time Series Forecast & Seasonality Analyzer allows you to project future data points from your CSV, JSON, or spreadsheet time-series data. By selecting from lightweight models like ARIMA, Exponential Smoothing, or Linear Trend, you can generate future periods with confidence bands while simultaneously decomposing your data into trend, seasonal, and residual components in a single comprehensive report.
When to Use
- •When you need to project future metrics like sales, traffic, or inventory levels based on historical time-series data.
- •When you want to isolate and analyze the underlying trend and seasonal patterns within a dataset.
- •When you require a quick, browser-based forecasting tool without writing complex Python or R scripts.
How It Works
- •Paste your time-series data as CSV or JSON, or upload a CSV, JSON, XLS, or XLSX file.
- •Specify the exact names of your timestamp and numeric value columns.
- •Select a forecasting model (ARIMA, Exponential Smoothing, or Linear Trend), set the forecast periods, and define the season length.
- •Generate the report to view the projected data with confidence intervals and export the forecast rows as JSON or CSV.
Use Cases
Examples
1. Forecasting Weekly Retail Sales
Retail Analyst- Background
- A retail analyst has daily sales data for the past year and needs to predict the next week's performance.
- Problem
- The data has strong weekend spikes, making simple averages inaccurate for daily predictions.
- How to Use
- Upload the daily sales CSV, set the timestamp and value columns, choose the ARIMA model, set forecast periods to 7, and season length to 7.
- Example Config
-
{ "timestampColumn": "date", "valueColumn": "sales", "model": "arima", "forecastPeriods": 7, "seasonLength": 7, "confidenceLevel": "0.95" } - Outcome
- The tool generates a 7-day forecast that accurately reflects the weekend sales spikes, along with a decomposition chart showing the isolated weekly seasonality.
2. Projecting Monthly Website Traffic
SEO Manager- Background
- An SEO manager tracks monthly organic traffic and wants to set realistic goals for the next two quarters.
- Problem
- Traffic grows steadily but drops every December, requiring a model that captures both the upward trend and the annual dip.
- How to Use
- Paste the monthly traffic JSON, select Exponential Smoothing, set forecast periods to 6, and season length to 12.
- Example Config
-
{ "timestampColumn": "month", "valueColumn": "visits", "model": "exponential-smoothing", "forecastPeriods": 6, "seasonLength": 12, "exportFormat": "csv" } - Outcome
- A 6-month traffic projection with 95% confidence bands, clearly showing the expected trend and the isolated seasonal impact of December.
Try with Samples
json, csv, xmlRelated Hubs
FAQ
What forecasting models are supported?
The tool supports a lightweight ARIMA(1,1,0) approximation, Holt-style Exponential Smoothing, and Linear Trend regression with seasonal adjustment.
What file formats can I upload?
You can upload CSV, JSON, XLS, and XLSX files, or paste raw CSV and JSON text directly into the input field.
How does the season length parameter work?
The season length defines the cycle length (e.g., 7 for weekly patterns in daily data) used to calculate seasonal adjustments and decompose the series.
Can I adjust the confidence intervals for the forecast?
Yes, you can select a confidence level of 80%, 90%, 95%, or 99% to control the width of the forecast bands.
How do I export the forecasted data?
You can choose to preview and export the generated forecast rows in either JSON or CSV format using the Export Format dropdown.