Time-series forecasting

Predict future values from past ones. The dataset is a single CSV where every row is one time step: a timestamp column, numeric feature columns and a numeric target. Rows must be in chronological order.

Folder layout

/data/shared/energy-demand/
└── demand.csv

Data CSV

timestamp,day_of_week,month,day_of_month,week_of_year,is_weekend,lag_1,moving_avg_7,value
2023-10-01,7,10,1,40,1,,,125.50
2023-10-02,1,10,2,40,0,125.50,,132.30
2023-10-03,2,10,3,40,0,132.30,,128.75
2023-10-07,6,10,7,40,1,152.40,132.75,148.60

Column	Meaning
`timestamp`	Fixed name. One value per row in ISO 8601 (`YYYY-MM-DD` or `YYYY-MM-DD HH:MM:SS`). Rows must be sorted ascending; every value must parse; every value must be before today. Dates that read differently day-first and month-first (such as `03.04.2026`) are rejected — use ISO 8601.
feature columns	Numeric, declared in `schema`. Empty cells are allowed (stored as NULL) — lag and rolling-window features are typically blank at the start of the series.
target column (`value` here)	Numeric; named by `label.column`, not declared in `schema`.

Categorical features (region, segment, …) must be encoded as integers before ingest: every non-timestamp column in schema must be numeric.

ingest.yaml

apiVersion: tracebloc.io/v1
kind: IngestConfig
category: time_series_forecasting
table: energy_demand_train
intent: train
csv: /data/shared/energy-demand/demand.csv
schema:
  timestamp: TIMESTAMP     # required; TIMESTAMP, DATETIME or DATE
  day_of_week: INT
  month: INT
  day_of_month: INT
  week_of_year: INT
  is_weekend: INT
  lag_1: FLOAT             # blank on the first row
  moving_avg_7: FLOAT      # blank until 7 rows of history exist
label:
  column: value
  policy: bucket

Field	Required	Meaning
`csv`	yes	Path to the data CSV.
`schema`	yes	Must contain `timestamp` with a calendar type (`TIMESTAMP`, `DATETIME` or `DATE`) plus the numeric feature columns.
`label`	yes, object form	`column` names the target; `policy: bucket` keeps raw targets in your secure environment (the platform receives 64 hash buckets; stored rows keep the raw value).
`time_column`	leave out	The time column is always `timestamp`. Any other value is rejected at preflight.
`columns`	no	Per-column `unit` and `ordinal` facts.
`data_id`, `spec.csv_options`	no	See the contract.

What the ingestor checks

Check	Rejects
Time format	A `schema` without `timestamp`, or with `timestamp` typed as text or a number; a CSV without a `timestamp` column; unparseable values; day-first/month-first ambiguous dates.
Time ordered	Any pair of consecutive rows whose timestamps decrease.
Time before today	Any timestamp on or after today’s date.
Numeric columns	A non-numeric value in any schema column other than `timestamp`. Empty cells are allowed.
Data types	Duplicate headers, schema columns missing from the CSV, values that do not match their type (checked for every schema column except `timestamp`).

Plus the checks every ingest runs. There is no label-diversity check.

Sample dataset

The template ships 31 daily rows starting 2023-10-01 with calendar features (day_of_week, month, day_of_month, week_of_year, is_weekend as INT), lag and moving-average features (lag_1, moving_avg_7 as FLOAT, blank until enough history exists) and the target value — the rows shown above are from it. The ingest.yaml above ingests it as is; only csv: changes to wherever you staged the file.

Next steps

Stage the data and run the ingest: Prepare Data
Shared rules for every template: Dataset templates

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Time-series forecasting

Folder layout

Data CSV

ingest.yaml

What the ingestor checks

Sample dataset

Next steps

​Folder layout

​Data CSV

​ingest.yaml

​What the ingestor checks

​Sample dataset

​Next steps

Folder layout

Data CSV

ingest.yaml

What the ingestor checks

Sample dataset

Next steps