Data Analysis
Forecast future periods from CSV or JSON time-series data and inspect trend, seasonal, and residual decomposition in one report
Call this tool from your code in three languages.
# 1) Upload each file first → returns { filePath }
curl -X POST 'https://api.elysiatools.com/upload/time-series-forecast-seasonality-analyzer' \
-F 'file=@/path/to/dataFile.ext'
# 2) Call the tool with the returned filePath values
curl -X POST 'https://api.elysiatools.com/en/api/tools/time-series-forecast-seasonality-analyzer' \
-F 'seriesInput=timestamp,value
2026-01-01,120
2026-01-02,128
2026-01-03,132
2026-01-04,125
2026-01-05,140
2026-01-06,148
2026-01-07,145
2026-01-08,126
2026-01-09,133
2026-01-10,138' \
-F 'dataFile=/path/to/file.ext' \
-F 'timestampColumn=timestamp' \
-F 'valueColumn=value' \
-F 'model=arima' \
-F 'forecastPeriods=8' \
-F 'seasonLength=7' \
-F 'confidenceLevel=0.95' \
-F 'exportFormat=json'Send a POST request with your inputs as JSON. File parameters require a separate upload first.
POST https://api.elysiatools.com/en/api/tools/time-series-forecast-seasonality-analyzer| Name | Type | Required | Description |
|---|---|---|---|
| seriesInput | textarea | No | — |
| dataFile | fileupload required | No | — |
| timestampColumn | text | Yes | — |
| valueColumn | text | Yes | — |
| model | select | No | — |
| forecastPeriods | number | No | — |
| seasonLength | number | No | — |
| confidenceLevel | select | No | — |
| exportFormat | select | No | — |
HTML result
{
"result": "<div>Processed HTML content</div>",
"error": "Error message (optional)",
"message": "Notification message (optional)",
"metadata": {
"key": "value"
}
}Add this tool to your Model Context Protocol server so AI agents can list and call it.
Add this block to your MCP client configuration:
{
"mcpServers": {
"elysiatools-time-series-forecast-seasonality-analyzer": {
"name": "time-series-forecast-seasonality-analyzer",
"description": "Forecast future periods from CSV or JSON time-series data and inspect trend, seasonal, and residual decomposition in one report",
"baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=time-series-forecast-seasonality-analyzer",
"command": "",
"args": [],
"env": {},
"isActive": true,
"type": "sse"
}
}
}After connecting to the SSE endpoint, list the exposed tools:
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/list"
}Invoke the tool by its id, passing arguments built from its parameters:
{
"jsonrpc": "2.0",
"id": 2,
"method": "tools/call",
"params": {
"name": "time-series-forecast-seasonality-analyzer",
"arguments": {
"seriesInput": "timestamp,value\n2026-01-01,120\n2026-01-02,128\n2026-01-03,132\n2026-01-04,125\n2026-01-05,140\n2026-01-06,148\n2026-01-07,145\n2026-01-08,126\n2026-01-09,133\n2026-01-10,138",
"dataFile": "https://example.com/file.ext",
"timestampColumn": "timestamp",
"valueColumn": "value",
"model": "arima",
"forecastPeriods": 8,
"seasonLength": 7,
"confidenceLevel": "0.95",
"exportFormat": "json"
}
}
}Questions or issues? Contact [email protected]