Time Series & Forecasting
Stationarity, seasonality, ARIMA vs ML forecasting, and temporal patterns
Time Series Foundations
- Time Series & Forecasting
Time Series Fundamentals
A time series is values in time order. Tomorrow depends on today, so shuffling breaks it and forecasting means reading that dependence.
- Time Series & Forecasting
Stationarity Testing With ADF and KPSS
A steady series keeps the same mean and spread over time. These two tests check that steadiness before you trust a forecast.
- Time Series & Forecasting
Seasonality and Trends in Time Series
A shop climbs all year then spikes every December. The climb is the trend, the December spike is seasonality, and mixing them up ruins forecasts.
- Time Series & Forecasting
Autocorrelation in Time Series
Slide a series against itself and score the match at each shift, so the shifts that line up expose the rhythm hiding in the wiggles.
- Time Series & Forecasting
Decomposing Time Series Into Parts
One sales number hides three stories. Split it into slow trend, repeating season, and leftover noise to see which one moved.
- Time Series & Forecasting
Time Series Cross-Validation
Shuffled folds let a forecast cheat by training on the future. Walk-forward splits train only on the past and test only on what comes next.
Time Series Modelling
- Time Series & Forecasting
ARIMA Models
Difference the series until its mean sits still, then forecast the next step from recent values plus recent mistakes.
- Time Series & Forecasting
SARIMA
SARIMA reads last month for momentum and the same month last year for season, so July learns from June and from last July at once.
- Time Series & Forecasting
Exponential Smoothing
Tomorrow looks most like today and less like last month. Exponential smoothing blends all past sales with weights that fall geometrically.
- Time Series & Forecasting
Prophet Forecasting
Prophet fits curves to the calendar itself, so trend, weekly rhythm, yearly season and holidays simply stack into the forecast.
- Time Series & Forecasting
LSTM for Forecasting
An LSTM carries a cell state across time steps, so a forecaster can remember last season while still reacting to this week.
- Time Series & Forecasting
Transformer Forecasting for Series
A transformer reads the whole past window at once. Attention weighs every old step directly, so last winter can speak to this winter.
- Time Series & Forecasting
Anomaly Detection in Time Series
Most points hug a baseline that drifts with trend and season, so subtract what you expect and flag the leftover that is too big to be noise.
- Time Series & Forecasting
Change Point Detection
One model stops fitting and two smaller ones fit far better, so cut the timeline where the saving is biggest and start fresh there.
- Time Series & Forecasting
Multi-Step Forecasting
One-step models answer tomorrow, while multi-step plans answer the next seven days, and the way you stretch the model decides where errors grow.
- Time Series & Forecasting
Probabilistic Forecasting
A point says demand will be 100, while a band says 92 to 108 with 90 percent confidence, and only the band tells you how much safety stock to hold.
- Time Series & Forecasting
Hierarchical Forecasting
Store forecasts must add to region forecasts and regions to the country. Reconciliation enforces the sums while borrowing strength across levels.