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How to forecast unevenly spaced time series data?
Since it is now a univariate time-series I have tried classical time-series forecasting methods like ARIMA and SARIMA (hasn’t worked out well) Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.
Can you convert unevenly spaced data to evenly spaced data?
Many analysts will immediately convert unevenly-spaced data to evenly-spaced time series to be compatible with existing sensor data analytics tools, but we have found that the conversion is usually unnecessary, and sometimes even causes problems. Unevenly-spaced data is actually pretty great
What to do with unevenly spaced sensor data?
When analysts are presented with unevenly-spaced sensor data, they usually convert the unevenly-spaced data to a evenly-spaced time series by regular sampling or linear interpolation. This conversion helps get the data into a format that are used by the most common tools for time series analysis.
How to model timeseries with unequally seasonality?
I have a timeseries that has an irregular seasonality interval, and two seasonalities. The data is basically daily data, with one weekly regular seasonality, and the other is a spike at the beginning of each month.
How to analyze unevenly spaced data in Python?
In response, we built a lightweight Python package called traces to simplify the reading, writing, and analysis of unevenly-spaced data. For example, if you want to know how many lightbulbs are turned on given the data from all light bulbs in your house, you can get this information using a very simple syntax.
Is there an example of time series forecasting in Python?
All code examples are in Python and use the Statsmodels library. The APIs for this library can be tricky for beginners (trust me!), so having a working code example as a starting point will greatly accelerate your progress. This is a large post; you may want to bookmark it.
Can a sensor be converted to an evenly spaced time series?
These event-triggered sensors give rise to unevenly-spaced time series. Many analysts will immediately convert unevenly-spaced data to evenly-spaced time series to be compatible with existing sensor data analytics tools, but we have found that the conversion is usually unnecessary, and sometimes even causes problems.