Time-series - TechnoScripts

time-series

Working with time series data in Python, from datetime handling to analysis and forecasting basics.

Choosing an AI Tech Stack: 3 Real Case Studies

Teams pick the shiny tool first, bend the problem to fit it, and six months later a large language model is doing a job a hundred lines of scikit-learn would have nailed. Choosing an AI tech stack is where projects quietly go wrong before any model code exists. This post gives you a four-axis decision framework, then walks three real case studies to very different, defensible stacks. “The best stack is the boring one that clears your acceptance metric and ... Read More

DL: RNNs and LSTMs for Sequence Processing

RNN LSTM models let you process sequential data that a plain feedforward network cannot, by reading one step at a time and remembering what came before. You will understand hidden states, the vanishing gradient problem in sequences, and how LSTM gates fix it, then build a simple RNN and a bidirectional sequence classifier in PyTorch, with every output run on a real machine. “LSTMs can learn to bridge time intervals in excess of 1000 steps. No other RNN architecture could ... Read More

ML: Time Series Analysis with ARIMA and Decomposition

Python time series work comes down to three moves: decompose a series into trend and seasonality, test for stationarity, and forecast with ARIMA (AutoRegressive Integrated Moving Average). We predict sales, stock prices, and server metrics using statsmodels, with every number coming from real local runs. “All models are wrong, but some are useful.” In time series, the useful ones are the ones that respect that the future leans on the past. George Box (statistician), with a time series twist Last ... Read More