Time series data refers to a series of data arranged in chronological order. Time series data are data that are observed in a specific time sequence, which could be annual, quarterly, monthly, daily or hourly. Trend, cycle and seasonal variation could be observed from a time series data, so it is important to conduct ANOVA tests and multiple comparisons to identify periodicity and circulation.
Time series processing (time series analysis) refers to the study of different methods used to interpret and understand these time series. It looks to study and understand of the laws by which a specific time series is generated.
Time series prediction predicts an outcome at a entered point of time by creating a mathematical model from a given time series data. These predictions methods are often used in various engineering or financial industries.
You can understand and analyze the overall process by looking at patterns of time series data and modeling the time series system.
Ex) Modeling of summer hourly energy consumption and predicting energy demand by controlling variables(temperature, population, hour, day of a week).
✓ Future Prediction
Future predictions could be made by modeling collected data if the trained time series pattern is maintained.
Ex) Future fuel price prediction based on past fuel price data analysis.
✓ Financial Market Prediction
Finances is one the industries that most commonly uses time series predictions. MA and AR models are often combined to ARMA models for effective prediction models.
Simple Moving Average Model
Simple moving average gives the same weight value for all time points to calculate the average.
Weighted Moving Average Model
Weighted moving average model gives different weight values for different time point to calculate the average.
Exponential Moving Average Model
Exponential moving average model gives more weight on the important recent data to exponentially calculate the average.
CLICK AI provides codeless artificial intelligence model development experience for users. With a just few clicks, users can develop machine learning models at a professional level through the automated process, and can build practical models to maximize business revenues.
On the [Model] tab, choose a model you would like to use and click the [Single prediction] to make single predictions.
Enter '20180401,' the last date of the training data, and click 'run.'
You can see the prediction result of the date you entered.