Updated: Feb 27, 2020
Time series forecasting is one of the core skills any data scientist is expected to master. There are a number of different techniques, which one can use: -
1. Naive Approach: The value of the new data point is predicted to be equal to the previous data point. The result would be a flat line, since all new values take the previous values.
2. Simple Average: The next value is taken as the average of all the previous values. It is better than the ‘Naive Approach’ as it doesn’t result in a flat line but in this all the past values are taken into consideration which might not always be useful.
3. Moving Average: Instead of taking the average of all the previous points, the average of ‘n’ previous points is taken to be the predicted value.
4. Weighted moving average: A weighted moving average is a moving average where the past ‘n’ values are given different weights.
5. Simple Exponential Smoothing: In this technique, larger weights are assigned to more recent observations than to observations from the distant past.
6. Holt’s Linear Trend Model: This method takes into account the trend of the data-set. By trend, we mean the increasing or decreasing nature of the series.
7. Holt Winters Method: This algorithm takes into account both the trend and the seasonality of the series. For example, the number of bookings in a hotel is high on weekends and low on weekdays, and increases every year. There exists a weekly seasonality and an increasing trend.
8. ARIMA: It is a popular statistical method for time series forecasting. ARIMA stands for Auto-Regressive Integrated Moving Averages. It describes the correlation between data points and takes into account the difference of the values.
ARIMA models work on the following assumptions –
A. The data series is stationary, which means that the mean and variance should not vary with time. A series can be made stationary by using log transformation or differencing the series.
B. The data provided as input must be a uni-variate series, since ARIMA uses the past values to predict the future values.
For further information and collaboration in understanding Time Series modeling, contact us to firstname.lastname@example.org