Serial differencing aggregation

Serial differencing is a technique where values in a time series are subtracted from itself at different time lags or periods. For example, the datapoint f(x) = f(x_t) - f(x_t-n), where n is the period being used.

A period of 1 is equivalent to a derivative with no time normalization: it is simply the change from one point to the next. Single periods are useful for removing constant, linear trends.

Single periods are also useful for transforming data into a stationary series. In this example, the Dow Jones is plotted over ~250 days. The raw data is not stationary, which would make it difficult to use with some techniques.

By calculating the first-difference, we de-trend the data (e.g. remove a constant, linear trend). We can see that the data becomes a stationary series (e.g. the first difference is randomly distributed around zero, and doesn’t seem to exhibit any pattern/behavior). The transformation reveals that the dataset is following a random-walk; the value is the previous value +/- a random amount. This insight allows selection of further tools for analysis.

Figure 9. Dow Jones plotted and made stationary with first-differencing

Larger periods can be used to remove seasonal / cyclic behavior. In this example, a population of lemmings was synthetically generated with a sine wave + constant linear trend + random noise. The sine wave has a period of 30 days.

The first-difference removes the constant trend, leaving just a sine wave. The 30th-difference is then applied to the first-difference to remove the cyclic behavior, leaving a stationary series which is amenable to other analysis.

Figure 10. Lemmings data plotted made stationary with 1st and 30th difference

{
  "serial_diff": {
    "buckets_path": "the_sum",
    "lag": 7
  }
}

resp = client.search(
    size=0,
    aggs={
        "my_date_histo": {
            "date_histogram": {
                "field": "timestamp",
                "calendar_interval": "day"
            },
            "aggs": {
                "the_sum": {
                    "sum": {
                        "field": "lemmings"
                    }
                },
                "thirtieth_difference": {
                    "serial_diff": {
                        "buckets_path": "the_sum",
                        "lag": 30
                    }
                }
            }
        }
    },
)
print(resp)

response = client.search(
  body: {
    size: 0,
    aggregations: {
      my_date_histo: {
        date_histogram: {
          field: 'timestamp',
          calendar_interval: 'day'
        },
        aggregations: {
          the_sum: {
            sum: {
              field: 'lemmings'
            }
          },
          thirtieth_difference: {
            serial_diff: {
              buckets_path: 'the_sum',
              lag: 30
            }
          }
        }
      }
    }
  }
)
puts response

const response = await client.search({
  size: 0,
  aggs: {
    my_date_histo: {
      date_histogram: {
        field: "timestamp",
        calendar_interval: "day",
      },
      aggs: {
        the_sum: {
          sum: {
            field: "lemmings",
          },
        },
        thirtieth_difference: {
          serial_diff: {
            buckets_path: "the_sum",
            lag: 30,
          },
        },
      },
    },
  },
});
console.log(response);

POST /_search
{
   "size": 0,
   "aggs": {
      "my_date_histo": {                  
         "date_histogram": {
            "field": "timestamp",
            "calendar_interval": "day"
         },
         "aggs": {
            "the_sum": {
               "sum": {
                  "field": "lemmings"     
               }
            },
            "thirtieth_difference": {
               "serial_diff": {                
                  "buckets_path": "the_sum",
                  "lag" : 30
               }
            }
         }
      }
   }
}