In the previous blog post, we discussed various types of time series data. We understood the concepts of stationarity and shocks. In this blog post, we will continue to discuss how we can estimate the predictability of time series data. People say that future is unpredictable. But that’s grossly reductive! What they actually mean to say is — I’m blindly assuming that my time series data is non-stationary, so I cannot accurately predict what’s going to happen in the future. Predicting future values can open a lot of doors in the Internet of Things (IoT) ecosystem. Before we can forecast future values, it’s important to determine if the time series data exhibits any properties that can be modeled. If not, we are just dealing with chaos and no model will be good enough. But a lot of data in the real world exhibits patterns, so we just need to look at it the right way. Let’s see how we can check if the given time series data has any underlying trends, shall we?   Continue reading “Estimating The Predictability Of Time Series Data – Part II” →

Time series data refers to a sequence of measurements made over time. The frequency of these measurements are usually fixed, say once every second or once every hour. We encounter time series data in a variety of scenarios in the real world. Some examples include stock market data, sensor data, speech data, and so on. People like to build forecasting models for time series data. This is very relevant in modeling data in the world of Internet of Things (IoT). Based on the past data, they want to predict what’s going to happen in the future. Once of the most important questions is to see whether or not we can predict something in the first place. How do we determine that? How do we check if there are underlying patterns in the time series data?   Continue reading “Estimating The Predictability Of Time Series Data – Part I” →