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Amazon

Equity · Monthly series · EQ-AMZN.NASDAQ

Latest, October 2026

240 USD

-3.5% from September 2026

Open interactive chartAmazon in the app

700.536937.49November 2016October 2027
History through October 2026, the forecast path to October 2027 (dashed) and its 80% interval (shaded).

Forecast

Forecast for October 2027: 431 USD, with an 80% interval of 265 USD to 646 USD.

Forecast path. Intervals are shown where available.
PeriodForecast80% low80% high
November 2026268 USD245 USD295 USD
December 2026297 USD237 USD346 USD
January 2027337 USD281 USD362 USD
February 2027359 USD257 USD420 USD
March 2027374 USD326 USD402 USD
April 2027394 USD310 USD398 USD
May 2027404 USD299 USD464 USD
June 2027416 USD248 USD469 USD
July 2027416 USD238 USD616 USD
August 2027415 USD264 USD619 USD
September 2027416 USD264 USD700 USD
October 2027431 USD265 USD646 USD

Model: Hierarchical neural network (neural network). Forecast generated 2026-11-13.

AI confidence

Weak evidence

The serving forecast made larger errors than the no-change baseline in these tests.

Model: Hierarchical neural network (neural network)

Accuracy vs naive
-35%Trails the no-change baseline over 6 tests (6 tests, 1 won)
Direction
51%No clear edge over a coin flip (37 of 72 calls right in 6 tests)
Turning points
10 of 32Anticipated some turns (32 turns in 6 tests, 0 without a turn)
Error vs baseline
35% higherError ratio 1.35; 1.00 is a tie.
Tests won
1 / 6Complete recent tests excluded from model selection; ties count as no win.
Test period
2020-11-01 to 2026-10-0112 observations ahead per test.
80% band coverage
81%Observed outcomes inside the band across all stored test windows.
How this assessment is made

AI confidence describes historical evidence for this forecast, not a probability of being correct. Each backtest fits on data available before its test window. The baseline carries the last value forward. Supported evidence requires at least six complete, non-overlapping tests, an error ratio below 0.90 (rounded to two decimals), and wins in a majority of tests. Ratios from 0.90 to below 1.10 are on par; 1.10 or above is weak evidence. Inconsistent wins produce mixed evidence. For a selected model, only the recent tests excluded from model selection determine this label. These are historical simulations using the available data, not a live trading track record; revisions and foundation-model pretraining can limit how closely they reproduce a past decision. Past performance can change.

Replay past tests

Model comparison

5 of 5 models evaluatedModels are selected on older tests and evaluated on separate recent tests. All four classes completed their backtests.

Models tested

Hierarchical neural network scored lowest on the 6 older tests (0.70) and was chosen. The 6 recent tests played no part in the choice and give its grade (1.35). The search for a model that beats the naive forecast on both sets of tests goes on. Scores are MASE: a model's errors divided by the naive forecast's, so 1 is a tie.

4 model classes completed the tests: Neural network, Machine learning, Foundation model, Statistical.

Every model tested on this series.
ModelClass and computeOlder testsRecent testsRole
N-HiTS (neural network)Neural network · CPU0.701.35Serving
CatBoost (machine learning)Machine learning · CPU0.760.96Tested
ci-proto (foundation model)Foundation model · CPU0.871.08Tested
Timestral (foundation model)Foundation model · CPU0.970.99Tested
AutoETS (statistical)Statistical · CPU0.970.99Tested
Naive forecast (last value held)Benchmark · CPU1.001.00Benchmark

Uses ci-proto, released by Continuous Intelligence. Continuous Intelligence does not endorse or approve this product or service. (ci-proto is shown as State-space model, 167M.)

Reading

It is above 95% of the 119 earlier readings since November 2016.

Written from the figures by a fixed template; no AI model is involved.

Recent history (24 observations)
The 24 most recent observations, newest first.
PeriodValue
October 2026240 USD
September 2026249 USD
August 2026260 USD
July 2026272 USD
June 2026238 USD
May 2026271 USD
April 2026265 USD
March 2026208 USD
February 2026210 USD
January 2026239 USD
December 2025231 USD
November 2025233 USD
October 2025244 USD
September 2025220 USD
August 2025229 USD
July 2025234 USD
June 2025219 USD
May 2025205 USD
April 2025184 USD
March 2025190 USD
February 2025212 USD
January 2025238 USD
December 2024219 USD
November 2024208 USD

Source

Data: public market data feeds.

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