Leverage machine learning to predict future trends, optimize resource allocation, and make strategic decisions with confidence.
Traditional spreadsheet forecasting can't adapt to changing market conditions or capture complex patterns in your data.
Without predictive insights, businesses react to trends after they happen, missing opportunities and wasting resources.
Manual forecasting methods struggle with seasonality, trends, and external factors, leading to costly planning errors.
Poor forecasts result in overstocking, understaffing, or misallocated budgets that directly impact your bottom line.
Our advanced forecasting platform uses state-of-the-art machine learning algorithms to deliver accurate, actionable predictions for your business.
Our ML models automatically detect seasonality, trends, cyclical patterns, and anomalies in your historical data. Unlike manual methods, these algorithms learn from thousands of data points simultaneously, identifying subtle patterns that humans might miss.
Generate forecasts for any time horizon your business needs - from daily predictions for operational planning to quarterly forecasts for strategic initiatives. Our models provide confidence intervals so you understand the uncertainty in each prediction.
As new data becomes available, our forecasting system automatically retrains and updates predictions. This ensures your forecasts stay accurate even as market conditions change, giving you a true competitive advantage.
Ready to eliminate forecasting guesswork? Our predictive analytics platform integrates seamlessly with your existing data infrastructure.
Time series data is any data collected over time at regular intervals - sales figures, website traffic, sensor readings, stock prices, or customer behavior metrics. The key characteristic is that observations are dependent on time, making them perfect candidates for forecasting.
Traditional statistical methods like moving averages or exponential smoothing provide basic forecasts but struggle with complex patterns. Machine learning approaches, particularly deep learning models like LSTMs and advanced ensemble methods, can capture intricate relationships and deliver superior accuracy.
AutoRegressive Integrated Moving Average (ARIMA) models are statistical workhorses for time series forecasting. We use automated parameter selection to build optimal ARIMA models, and extend them with Seasonal ARIMA (SARIMA) for data with clear seasonal patterns like retail sales or tourism.
Facebook's Prophet algorithm is specifically designed for business time series with strong seasonal effects and historical trend changes. It's particularly effective for metrics like daily active users, revenue, or website conversions where holidays and special events matter.
Long Short-Term Memory (LSTM) networks excel at learning long-term dependencies in sequential data. We deploy LSTMs when your time series has complex, non-linear patterns that traditional methods can't capture - like customer behavior prediction or multivariate demand forecasting.
By combining multiple forecasting models (ARIMA, Prophet, LSTM, XGBoost), we create ensemble forecasts that are more robust and accurate than any single model. This approach reduces forecast variance and provides better predictions across different time horizons.
Time series forecasting drives better decisions across every department:
Based on our experience implementing forecasting solutions across industries, here are critical best practices:
A Nordic retail chain was struggling with inventory management, frequently experiencing stockouts during peak periods while overstocking slow-moving items. Their Excel-based forecasting couldn't handle the complexity of 10,000+ SKUs across 150 stores.
We implemented an ensemble forecasting system combining Prophet for trend and seasonality detection, XGBoost for incorporating promotional and weather data, and LSTMs for capturing store-specific patterns. The system generates daily forecasts for each SKU at each location.
Results: 25% improvement in forecast accuracy (MAPE reduction from 32% to 24%), 15% reduction in overall inventory levels, 40% reduction in stockouts, and estimated annual cost savings of €2.3M through optimized inventory and reduced markdowns.
The minimum depends on your data frequency and patterns. For daily data, we recommend at least 2 years of history to capture seasonal patterns. For monthly data, 3-5 years is ideal. However, we can work with shorter histories by incorporating external variables and using transfer learning from similar datasets. Data quality matters more than quantity - clean, consistent data with fewer data points often outperforms noisy data with longer history.
Standard forecasting models struggle with unprecedented events like COVID-19 because they learn from historical patterns. However, we implement several strategies to improve resilience: ensemble models that combine multiple approaches, anomaly detection to flag unusual patterns, scenario analysis with different assumptions, and rapid retraining capabilities. We also recommend maintaining human oversight for strategic decisions, using forecasts as decision support rather than autopilot.
We use multiple metrics to evaluate forecasts: Mean Absolute Percentage Error (MAPE) for overall accuracy, Root Mean Square Error (RMSE) for penalizing large errors, and Mean Absolute Scaled Error (MASE) for comparing across different time series. We also perform time series cross-validation, testing the model on multiple future periods to ensure it generalizes well. Most importantly, we measure business impact - did the forecast help you make better decisions and improve outcomes?
A complete forecasting solution typically takes 8-12 weeks: Week 1-2 for data discovery and preprocessing, Week 3-6 for model development and testing, Week 7-8 for integration with your systems, Week 9-10 for user training and documentation, Week 11-12 for monitoring setup and refinement. For simpler use cases with clean data, we can deliver MVP forecasts in 4-6 weeks. Complex scenarios with multiple data sources or custom algorithms may take 12-16 weeks.
Our forecasting solutions integrate seamlessly with popular BI platforms like Tableau, Power BI, and Looker. We typically deploy forecasts via REST APIs that your BI tools can query, or write predictions directly to your data warehouse (Snowflake, BigQuery, Redshift, etc.). This allows your existing dashboards and reports to incorporate forward-looking predictions alongside historical data. We also provide Python and R packages for data science teams who want direct access to forecast models.
Transform uncertainty into opportunity with ML-powered forecasting. Our team will assess your data, identify forecasting opportunities, and design a custom solution for your business.
Based in Lund, Sweden • Serving businesses globally