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.

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Data Analysis

Time Series Forecast & Seasonality Analyzer

Forecast future periods from CSV or JSON time-series data and inspect trend, seasonal, and residual decomposition in one report

Details

What this tool helps you do

Import CSV, JSON, or spreadsheet time-series data with a timestamp column and a numeric value column. The tool fits one of three lightweight forecasting models, projects future periods with confidence bands, and decomposes the observed series into trend, seasonal, and residual components.

Model notes:

ARIMA uses a lightweight ARIMA(1,1,0)-style approximation that is practical for browser-side forecasting
Exponential smoothing uses a Holt-style level and trend update
Linear trend uses regression over the observed index plus seasonal adjustment

How to fill the form:

CSV or JSON Input: paste a table or JSON array of objects
Data File: upload CSV, JSON, XLS, or XLSX instead of pasting
Timestamp Column / Value Column: choose the field names to analyze
Forecast Model: select the forecasting approach to use
Forecast Periods: number of future rows to generate
Season Length: cycle length used for decomposition and seasonal adjustment
Confidence Level: controls the forecast interval width
Export Format: preview the generated forecast rows as JSON or CSV

Execution

Run this tool

Fill in the form, run the tool, and review the result in one place.

Prepared example runs

Click an example to fill the form automatically. File inputs still need an upload.

1 examples

Forecast weekly sales with seasonality

Project the next 8 periods from a retail sales series and inspect whether the weekly seasonal pattern is stable.

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Inputs

Options are grouped so long tools stay easier to scan.

9 options

FilesUpload source files for this workflow.1

Data FilefileOptional

Single file max size: 20 MBSupported types: text/csv, application/json, application/vnd.ms-excel, application/vnd.openxmlformats-officedocument.spreadsheetml.sheet, .csv, .json, .xls, .xlsx

ContentPaste or type the main input values.3

CSV or JSON InputtextareaOptionalTimestamp ColumntextRequiredValue ColumntextRequired

SettingsAdjust formats, ranges, numbers, and modes.5

Forecast ModelselectOptionalForecast PeriodsnumberOptionalSeason LengthnumberOptionalConfidence LevelselectOptionalExport FormatselectOptional

Result

Ready for a run

Run the tool to preview files, text, structured data, or streamed output here.

Samples

Time Series Forecast & Seasonality Analyzer

What this tool helps you do

Run this tool

Prepared example runs

Inputs

Result

Examples that match this tool

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Prepared example runs

Inputs

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Key facts

Overview

When to use

How it works

Use cases

Examples

1. Forecasting Weekly Retail Sales

2. Projecting Monthly Website Traffic

FAQ

XLS Samples

XLSX Samples

CSV Samples

Python Samples

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