Head-to-head · Time Series Intelligence Software
TrendMiner vs IBM SPSS Statistics
TrendMiner leads on 0 checks, IBM SPSS Statistics on 0, and 2 are even. Who comes out ahead on the 2 yes/no, price and count checks where we have data for both products. The editor score weighs everything else too.
Our verdict
- Highest scoreTrendMiner · 8.6/10
- Free trialonly IBM SPSS Statistics
- Most featuresTrendMiner · 1 of 4
TrendMiner scores higher on our rubric for time series intelligence software: 8.6 against 7.9 out of 10; our editors rank them #2 and #5.
TrendMiner is the better fit for industrial teams exploring and monitoring time series. IBM SPSS Statistics is the better fit for statisticians who need forecasting alongside analysis.
- TrendMiner fits best
Industrial teams exploring and monitoring time series
- IBM SPSS Statistics fits best
Statisticians who need forecasting alongside analysis
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Side by side
| Feature | TrendMiner 8.6/10 Visit ↗ | IBM SPSS Statistics 7.9/10 Visit ↗ |
|---|---|---|
| At a glance | ||
| Editor score | 8.6 | 7.9 |
| Ranking | #2 in Time Series Intelligence Software | #5 in Time Series Intelligence Software |
| Best for | Industrial teams exploring and monitoring time series | Statisticians who need forecasting alongside analysis |
| Pricing model | Paid | Paid |
| Starting price | Pricing on request | — |
| Free plan | Not checked yet | — |
| Free trial | — | 14 days |
| Deployment | Cloud, Self-hosted | Desktop |
| Platforms | Web, Linux | Windows, macOS |
| Support | Email, Phone, Docs | Community, Docs |
| Integrations | 11 integrations | 2 integrations |
| Built for | Mid-market, Enterprise | Small business, Mid-market, Enterprise |
| Features TrendMiner 1/4 · IBM SPSS Statistics 0/4 | ||
| Anomaly detection | ✓ (best) | Not checked yet |
| Change-point detection | Not checked yet | Not checked yet |
| Model monitoring | Not checked yet | Not checked yet |
| Forecast intervals | Not checked yet | Not checked yet |
| Specs | ||
| Forecasting methods | Not checked yet | Not checked yet |
| Forecast horizon | Not checked yet | Not checked yet |
| Deployment | Not checked yet | Not checked yet |
| Our review | ||
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| Cons |
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| Our verdict | TrendMiner is an industrial analytics platform for operational specialists in process manufacturing and other industrial environments. It brings time-series exploration, pattern searches, correlations, contextualization, monitoring, and… Read the review → |
IBM SPSS Statistics brings data preparation, statistical testing, regression, predictive modeling, and time-series forecasting together in desktop software. It is aimed at researchers, analysts, organizations, and professionals who need… Read the review → |
Strengths and trade-offs
TrendMiner — where it wins
- Search patterns, compare trends, and analyze correlations and fingerprints.
- Connect time-series data with event, asset, and contextual data.
- Monitor processes and apply anomaly detection and machine-learning models.
Where it doesn't
- Pricing requires contacting sales rather than choosing a self-service plan.
- Its focus on industrial operational data may not suit general-purpose analytics.
- Deployment and integration options may require specialist implementation.
IBM SPSS Statistics — where it wins
- Combines time-series forecasting with regression and statistical testing
- Adds decision trees and neural networks for predictive modeling
- Supports R and Python through APIs, embedded code, and extensions
Where it doesn't
- Desktop deployment limits it to Windows and macOS
- No free plan; the published free trial lasts 14 days
- Forecasting is one part of a broader statistical toolkit
- TrendMiner8.6/10 · Pricing on request
Industrial teams get focused tools for exploring, contextualizing, and monitoring time series.
Visit TrendMinerFull verdict → - IBM SPSS Statistics7.9/10 · 14-day trial
A broad desktop statistics suite with forecasting and optional analysis modules.
Visit IBM SPSSFull verdict →
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
Last updated · How we research and update





