Introduction to Time Series Analysis
Time-Series alludes to data recording at regular interims of time. Time-Series components involve –
Trend – It increases, decreases or remains at a constant level with respect to time.
Seasonality – It is the property of Time – Series to show periodical patterns that repeats at a constant frequency.
Cycles – They do not repeat at regular time intervals and occur regardless of whether the frequency is 1.
Challenges to Time Series Forecasting
Sensor Data or Stock Market data consists of ~250 factors ‘col1’,’col2’… and so forth and the data size is 15 gb.
Besides this, there are two critical categorical variables named ‘image,’ ‘categ’ and another variable ‘time.’
There are 22 unique symbols, and seven unique cats’s in the entire dataset recorded for every minute.
The target is to forecast ten future values of a column named ‘val’ for each symbol categ pair.
Solution Offered for Time Series Forecasting
Approaches for Time Series Analysis
Approach 1 – Convert Time Series Problem to Supervised Learning Problem.
Convert Time-Series data to Supervised Learning data.
Supervised Learning requires the values for all the independent variables.
Approach 2 – Using VAR(Vector Autoregression) Model.
It is an extension of the one dimensional Autoregressive Method. The advantage of this method is, no need to convert the Time Series Data to Supervised Learning Data.
Get the predictions for future values from the model itself. The model considers the interdependencies in the data.
Pattern Analysis with Time Series Data
Understanding Pattern Analysis and its Components
Pattern Analysis with Time Series Data includes nature identification proof spoke to by a sequence of observations and forecasting including prediction of future values Time-Series variable. Time Series Pattern Analysis consists of systematic pattern data called as a set of identifiable components and random noise error which makes pattern identification difficult.
Components of Pattern Analysis
Trend Analysis Overview
The trend is described as a linear function to eliminate non-linearity through a log or exponential functions. If an error occurs in trend, then smoothing is required such as a moving average with components replacement of the series with a simple or weighted average.
Seasonality Analysis Overview
It consists of autocorrelation correlograms to display sequential connection for consecutive lags and examining correlograms, removal of serial dependencies and partial autocorrelations.