ARIMA

I have been spending a lot of time around AI agents lately. Agents that can read files, call tools, update things, research things, write things, and generally make you feel like the computer might be about to become a coworker. Which is exciting. Also slightly stressful if you think about what it has access to for more than 30 seconds. Then there is ARIMA. ARIMA looks like it was named by someone who wanted to make sure nobody accidentally found statistics too exciting. It does not have a chat interface, cannot browse the web and definitely cannot write a follow-up email with “just circling back” in it. It looks at a sequence of numbers and asks some basic questions. ...

If you are trying to decide between ARIMA and SARIMA, the thing you are really checking for is seasonality. ARIMA is for a series with a trend or short-term dependence, but no predictable repeating cycle. SARIMA is for when you have that same stuff plus a repeating pattern, like monthly demand going up every December or daily traffic acting differently every weekend. Pretty much. The annoying bit is that seasonality can look like random mess until you actually plot the data. Also, a seasonal model sounds more complete, so it is tempting to throw one at everything. I have done that. Forecasting gets annoying pretty fast when the model is more complicated than the data needs it to be. ...

Deep Learning Gets the Spotlight, But Time Series Still Solves Real Problems In the machine learning landscape today, deep learning models - transformers, LSTMs, and other neural networks steal the show. They’re impressive, powerful, celebrated and make you feel smart too when you use them. However, when it comes to forecasting business metrics like sales, demand, or inventory, deep learning isn’t always the answer. Traditional time series models, especially ARIMA (AutoRegressive Integrated Moving Average) and its seasonal extension SARIMA, are some of the most effective and interpretable methods for forecasting structured temporal data. ...