Time Series Analytics
Forecasts and alerts your team can plan around
You have metrics that change every day: orders, traffic, fraud, usage, inventory. The hard questions are: what happens next? and when did something break? We build production forecasting and anomaly pipelines, not one-off charts in a notebook.
Time series use cases
From weekly demand plans to sub-second streaming metrics: models that refresh and monitoring that catches drift.
Demand & volume forecasting
Predict orders, traffic, inventory needs, or workload by day/week/month, with honest confidence intervals and seasonality handled correctly, not ignored.
Anomaly detection on metrics
Catch spikes, drops, and drift in revenue, fraud rates, API latency, or operational KPIs before they become incidents, or before the monthly review surprises you.
Seasonality & trend decomposition
Separate what's normal (holidays, day-of-week, weather patterns) from what's actually changing so leadership reads signal, not noise.
Event and changepoint analysis
Measure impact of launches, price changes, or policy shifts on time-based metrics, with before/after windows that finance and product can trust.
High-frequency & streaming series
Minute-level, hourly, or event-stream data at scale: rolling windows, real-time features, and pipelines that keep up with production volume.
Forecast monitoring & refresh
Models go stale. We ship scheduled retraining, accuracy tracking, and alerts when predictions drift, so forecasts stay usable months after launch.
Related case study · High-frequency time series at scale
Real-time fraud detection at 10TB+ daily
Deployed an ensemble ML pipeline processing full transaction volume in real time with sub-second scoring, monitoring, and incident runbooks. Federal-scale transaction streams are a time series problem: real-time scoring, drift monitoring, and production pipelines at 10TB+ daily volume.
Read full case study →6 weeks
POC to production
6 weeks from kickoff to production deploy
Questions we answer with data
How much demand should we expect?
Forecasts by day, week, or SKU, with seasonality and holidays handled, not ignored.
Is this metric behaving normally?
Alerts when revenue, fraud, latency, or usage diverges from expected bands.
Did the launch actually move the needle?
Before/after analysis on time-based KPIs, separating signal from normal noise.
Pair time series with geospatial analytics when growth questions need both where and when: regional demand trends, store-level seasonality, or location-based forecasting.
- Retail & e-commerce: demand, inventory, and promotional lift forecasting
- Financial services: transaction volumes, fraud patterns, and risk metrics over time
- SaaS & product: usage, churn signals, and growth metric monitoring
- Operations & logistics: workload planning, capacity, and SLA tracking
- Energy & utilities: load forecasting and asset telemetry trends
- Marketing & growth: campaign response curves and cohort behavior over time
Why teams choose PrismBase for time series
- Deep experience with messy real-world series: missing data, regime changes, and bad seasonality assumptions
- Forecasts tied to decisions: inventory, staffing, budgets, alerts, not vanity R² scores
- Production pipelines with monitoring, backtests, and refresh, not one-off notebook charts
- Honest about when simple methods beat deep learning, and when they don't
- Same principal from discovery through deployment under the Production Standard
The Production Standard
The Production Standard
Six commitments contracted in writing on every engagement.
01
Deploy or redirect
If AI isn't the right tool, we say so in week one and redirect budget to what will ship. No POC theater.
02
Metrics before models
Business KPIs locked in discovery. Model selection follows the metric, not the hype cycle.
03
Governance from day one
Model cards, access controls, evaluation harnesses, and audit trails, not a compliance bolt-on at go-live.
04
Production artifacts
Deployed code, monitoring dashboards, CI/CD pipelines, and incident runbooks. Not recommendations for someone else to implement.
05
6-week production target
Scoped projects designed for production in 6–10 weeks. Boutiques move; global programs wait for steering committees.
06
Capability transfer
Your team can operate, extend, and maintain what we build. IP and documentation transfer is included, not upsold.
Every scoped project includes a production deliverable checklist signed off before close. If we can't commit to deployable output, we won't take the engagement.
Frequently asked questions
What time series problems do you solve?
Demand and volume forecasting, KPI anomaly detection, seasonality analysis, changepoint detection after product or pricing changes, and production pipelines that keep models refreshed. We work with daily business metrics through high-frequency streaming data.
How is this different from a dashboard?
Dashboards show what happened. Time series models estimate what happens next and flag when reality diverges from expectation. We build the pipelines and monitoring so those predictions stay accurate in production.
What methods and tools do you use?
Classical methods (ARIMA, ETS, Prophet, seasonal decomposition) when they win; gradient boosting and deep learning when volume and features justify it. Python, SQL, Spark, and your warehouse or streaming stack. We ship code your team runs, not slide decks.
Can you work with our existing metrics and data warehouse?
Yes. Most engagements start from tables you already have (orders, events, logs, aggregates) and add forecasting or anomaly layers with clear ownership and refresh schedules.
How do engagements start?
Discovery call on the metric you need to predict or monitor, data history available, and how forecasts will be used. Then a scoped SOW with backtest targets and production deliverables.
Have metrics over time and planning questions?
Tell us what you need to forecast or monitor, what data history you have, and how outputs will be used. We'll scope production delivery, not another exploratory notebook.
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