8 docs tagged with "time-series"

AR, MA, ARMA, ARIMA, and SARIMA models - derivation, parameter estimation, Box-Jenkins methodology, diagnostic checking, and Python implementation with statsmodels. The classical forecasting baseline every ML engineer must know.

ACF and PACF functions, lag plots, correlograms, Ljung-Box test, and identifying ARIMA orders from autocorrelation structure. Essential for time series model selection in ML and forecasting.

Cointegration, Johansen test, error correction models, Granger causality, and their applications in pairs trading, causal feature selection, and financial ML. Essential for multi-series time series analysis.

Discrete Fourier Transform, Fast Fourier Transform, power spectrum, frequency-domain features, and Fourier-based positional encodings in transformers. Essential for audio ML, IoT, and sequence model design.

Overview of time series mathematics - stationarity, autocorrelation, Fourier analysis, ARIMA, state-space models, Kalman filter, cointegration, and wavelets. Critical for financial ML, IoT, and sequential model design.

State space representation, Kalman filter derivation, smoothing, sensor fusion, connection to RNNs and LSTMs, and implementation in Python. The mathematical backbone of optimal sequential estimation.

Engineering guide to strict and weak stationarity, ergodicity, unit roots, Augmented Dickey-Fuller test, differencing, and why failing to check stationarity breaks ML time series models.

Continuous and discrete wavelet transforms, mother wavelets, multiresolution analysis, wavelet denoising, and connections to WaveNet and modern audio neural networks. Simultaneous time-frequency analysis beyond Fourier.