Contents
Is time series a regression or classification?
A time series forecasting problem in which you want to predict one or more future numerical values is a regression type predictive modeling problem. Classification predictive modeling problems are those where a category is predicted.
What are the uses of time series?
A time series is a data set that tracks a sample over time. In particular, a time series allows one to see what factors influence certain variables from period to period. Time series analysis can be useful to see how a given asset, security, or economic variable changes over time.
Why is time series classification a challenging problem?
During the last years, Time Series Classification has become one of the most challenging problems in Data Science. This has happened because any classification problem that uses data keeping in consideration some notion of sorting, can be treated as a Time Series Classification problem.
How to cross validate binary classification on time series data?
You can think of state models for time series like Multi State Modelling (MSM) or Hidden Markov Models ( if data has markov property). In these state models you can have beginning reading, inflow, outflow as external variables and then you can build your model. Thanks for contributing an answer to Cross Validated!
How to do a timeseries classification from scratch?
This example shows how to do timeseries classification from scratch, starting from raw CSV timeseries files on disk. We demonstrate the workflow on the FordA dataset from the UCR/UEA archive.
How are classes represented in a time series?
Every class in the dataset must be represented with its one-hot label vector. A one-hot label vector is a vector with size equal to the number of different classes in the dataset. Each element of the array corresponds to a possible class, and every value is 0 apart from that related to the represented class, that is 1.