Predicting future airfare using past data I have chosen the topic of "Predicting future airfare using past data" for my project and would love to get inputs on the best models to use. 

Data Set

The data set consists of 6 months of time series data for every onward and return journey dates spaced by 8 days for a particular route. So, for the trip of 1 Oct-9 Oct, 2017, I have the flight price checked every day from 1 Apr to 30 Sep. I have this data for every trip date from 1 July, 2017 to 15 Oct, 2017.

Data Format

(Fake Data)

+--------------------+-----------+--------------------+--------------------------+---------------+
| requestDate        | price     | tripStartDeparture | tripDestinationDeparture | flightCarrier |
+--------------------+-----------+--------------------+--------------------------+---------------+
| 14APR2017:00:00:00 | 725.32    | 16SEP2017:10:50:02 | 23SEP2017:21:55:04       | XA            |
+--------------------+-----------+--------------------+--------------------------+---------------+
| 15APR2017:00:00:00 | 966.32    | 16SEP2017:13:20:02 | 23SEP2017:19:00:04       | XA            |
+--------------------+-----------+--------------------+--------------------------+---------------+
| 16APR2017:00:00:00 | 915.32    | 16SEP2017:13:20:02 | 23SEP2017:21:55:04       | XA            |
+--------------------+-----------+--------------------+--------------------------+---------------+
+--------------------+-----------+--------------------+--------------------------+---------------+
| 16APR2017:00:00:00 | 825.32    | 16SEP2017:10:50:02 | 23SEP2017:21:55:04       | XA            |
+--------------------+-----------+--------------------+--------------------------+---------------+
| 16APR2017:00:00:00 | 969.32    | 11SEP2017:13:20:02 | 18SEP2017:19:00:04       | XA            |
+--------------------+-----------+--------------------+--------------------------+---------------+
| 16APR2017:00:00:00 | 918.32    | 06SEP2017:13:20:02 | 13SEP2017:21:55:04       | XA            |
+--------------------+-----------+--------------------+--------------------------+---------------+


Data Exploration

Variation in price for different start dates. 
X-axis: (Observation date - start date).days, 
Y-axis: Price in USD



Goal

For a given trip dates, the goal is to predict the price in the future, given the price today.

Approach

Despite the data being for 6 months for each trip date, airfares are mostly constant in until about 120 days before departure. It becomes quite noisy in those 120 days. Most probably, because 120 data points are little for time series forecasting, ARIMA did not perform very well. However, I think it is possible to create a way better model utilizing the data for all the data for all trips from 1 July, 2017 to 15 Oct, 2017.

I would love to hear about possible approaches. Thank you for reading this wall of text.
I liked the way you put across your question!

I think we cannot cannot say in specific will work well with data, it is most likely trial &amp; error method, If ARIMA is not performing well and assuming that there is no trend in data then you can use AR, Exponential Smoothening. These are basic techniques but as you know in many scenarios basic models can explain better than complex models.

These both works well in such scenarios. Give a try.

It would be nice if you can share some graphs which can explain us a bit more about the how the data is, noise etc.
I mean if you have anything from your exploratory analysis.

Hope my answer is helpful!