Time series data is everywhere sales, GDP, weather, stock prices. We decompose every series into trend, seasonality, cycles, and irregular noise. Our approach identifies stationarity, autocorrelation, and underlying structure to build robust forecasting models.
We apply classical and modern statistical learning to match your data's unique temporal dependencies.
Autoregressive Integrated Moving Average with seasonal extensions. Handles non‑stationary series via differencing. Box‑Jenkins methodology, ACF/PACF diagnostics, Ljung‑Box test residuals.
Holt‑Winters & ETS error-trend-seasonal frameworks. Great for short-term forecasts with strong trend and seasonality. Automatic model selection via AICc.
Multivariate time series: analyze interactions between multiple variables, impulse response functions, and Granger causality tests essential for macroeconomic forecasting.
Robust to missing data and outliers, handles holiday effects, changepoints, and intuitive parameter tuning. Great for business forecasting.
Volatility modeling for financial returns ARCH/GARCH, EGARCH for asymmetric effects, volatility clustering and risk forecasting (VaR).
Recurrent neural networks for long sequence dependencies, probabilistic forecasting with Amazon DeepAR, ideal for high‑frequency data.
Stationarity tests (ADF, KPSS), decomposition, missing value treatment, outlier detection.
ACF/PACF, information criteria (AIC/BIC), cross-validation for hyperparameter tuning.
Residual analysis (normality, homoscedasticity), Ljung‑Box, rolling forecast evaluation.
Production pipelines, dashboards, automated forecast refresh with anomaly alerts.
Demand forecasting, inventory optimization, promotional lift analysis, and anomaly detection.
Stock price prediction, volatility modeling (GARCH), macroeconomic indicators (GDP, CPI).
Patient flow forecasts, infection rate modeling, resource allocation, time-to-event analysis.
Our experts will analyze your data frequency, seasonality patterns, and recommend the optimal model stack (ARIMA, Prophet, LSTM, etc.).