# Forecasting technique for an increasing upward trend

I have a time series data which increases by a certain value and then remains constant for certain period of time. The increase may be very high or a normal increase. I have to forecast the values for next two years. I have used ARIMA and Holt-Winters Method. Should i select the best fit model by minimum MASE value? Is there any other method to forecast such time series?

Original Time Series Plot

Forecasted using ARIMA

Forecasted using Holt-Winters Method

Edit 1 - After implementing the suggestion this is what i am getting.

Step 1 - Using Diff to remove the trend

Step 2 - Using Diff and log together to remove heteroscedasticity.

Step 3: Forecasting using auto.arima on diff of original time series. Auto.arima is not able to provide any recommended model (p=0,d=0,q=0)!!

• apply log to the series before ARIMA, don't forget to exponentiate it after – Aksakal Mar 23 '18 at 3:00
• I have used auto.arima. Wouldn't that work – ANURAG GUPTA Mar 23 '18 at 6:24

The first thing I would suggest is to work not on the original series, but on first differences. Then the changes in slope will turn into changes in level. There is a lot of literature on detecting level changes; the strucchange package for R and the literature cited there would be a good place to start reading.
Once you know when the level shifts occurred, you can treat them historically, by including dummy values for each step, e.g., by first regressing your differenced series on the dummies, then running ARIMA or even Holt-Winters on the residuals - auto.arima() in R does this.