Number of Observations
Time series forecasting relies heavily on the amount of data available. So the concept of “more data equals better analysis” also applies to time series forecasting. The more observation points we have, the better our models will fit our data.
Time Horizon
The time horizon is how far into the future you are pr!icting. The further out you try to go, the less reliable your pr!ictions will be. If your dataset contains a lot of short-term data, you should avoid creating pr!ictions with a long time horizon.
Random Data
Random events can never be pr!ict! accurately. So it doesn’t matter how much data we collect on random events or how consistently we collect that data. For example, lottery numbers can be record! every week, but you can’t pr!ict the next winning numbers. The lottery is design! to be random. Therefore, historical patterns will not reflect future patterns, and any observ! similarities will be coincidental.
The use of quality data is a standard requirement
for any data analysis technique. Typical guidelines for data quality include ensuring that data is not duplicat!, is collect! in a valid format, is collect! consistently, and is collect! at regular intervals. Of these, it is particularly important that time series data are collect! at regular intervals for the time period being analyz!.
Stability
Stationary a model of good practice data can change over time, but some basic properties always remain the same. In order to build a time series model that can reliably pr!ict future values, the data we use must be stationary. There are tests you can run on your dataset to find out if your data is stationary. A common test is the Dickey-Fuller test . There are also transformations that can take your data from non-stationary to stationary while still preserving the properties ne!! to perform a time series forecast.
Esri’s time series forecasting toolkit has four different forecasting tools, depending on the type of data being forecast! and the type of output the user requires. The toolkits are also design! to spatially visualize the distribution of forecasts to aid in spatial decision making.
You can find documentation for each tool in Esri’s time series forecasting toolkit here
Curve Fit Forecast
Time series data comes in all shapes and sizes. how has gdpr legislation affect! marketing efforts in spain ? Some may have a linear trend, while others alb directory may follow a more S-shap! curve. This toolkit is design! to help you deal with this level of variability in time series data. If you don’t know the curve that best describes your area of study, you can choose “auto detect” and the tool will automatically suggest the most appropriate statistical model to use for your pr!ictions.