LSTMTimeStep forecast always descending or ascending consecutive numbers

What is wrong?

Maybe it’s something wrong in my code but I expected that every forecast for each day predict based on the past days not always a descending or ascending numbers from the first day.

Where does it happen?

• Windows 10
• Node.js v12.16.2
• brain.js@2.0.0-alpha.12

How do we replicate the issue?

• The data is normalized
• At 60k interactions get errorThresh = 0.005
• There is no error, just the forecast output always are descending or ascending consecutive numbers

Code sample:

const netOptions = {
	inputSize: 4,
	hiddenLayers: [20, 20],
	outputSize: 4
};

const net = new brain.recurrent.LSTMTimeStep(netOptions);

const trainingOptions = {
	iterations: 1000, 
	errorThresh: 0.002,
	learningRate: 0.005
};

const trainingData = [
  [
	...9k more data
    {
      close: 0.3672922007633208,
      open: 0.3753351139350292,
      high: 0.37622875109510723,
      low: 0.3603217093776281
    },
    {
      close: 0.37819481004120087,
      open: 0.37319032398229984,
      high: 0.3817694480470161,
      low: 0.36872204881640663
    }
  ]
];

const stats = net.train(trainingData, trainingOptions);

const toPredictData = [
		normalData.slice(-40, -20),
	];

const forecast = net.forecast([
	toPredictData[0][0],
	toPredictData[0][1],
	toPredictData[0][2],
	toPredictData[0][3],
	toPredictData[0][4],
	toPredictData[0][5],
	toPredictData[0][6],
	toPredictData[0][7],
	toPredictData[0][8],
	toPredictData[0][9],
	toPredictData[0][10],
	toPredictData[0][11],
	toPredictData[0][12],
	toPredictData[0][13],
	toPredictData[0][14],
	toPredictData[0][15],
	toPredictData[0][16],
	toPredictData[0][17],
	toPredictData[0][18],
	toPredictData[0][19],
], 20);

Output:

• This is the output at 60k interactions with errorThresh 0.005.
• Always are descending or ascending.
• ‘pre’ = close value taken from the forecast result.
• ‘ori’ = original close value from the data.

{
 "0": { "day": 0, "ori": 302.73999, "pre": 281.6479115675968,"err": 6.97 },
 "1": { "day": 1, "ori": 292.920013, "pre": 280.9524207791296, "err": 4.09 },
 "2": { "day": 2, "ori": 289.029999, "pre": 280.2686673253774, "err": 3.03 },
 "3": { "day": 3, "ori": 266.170013, "pre": 279.0121085019256, "err": 4.83 },
 "4": { "day": 4, "ori": 285.339996, "pre": 277.6469256257541, "err": 2.7 },
 "5": { "day": 5, "ori": 275.429993, "pre": 276.35117074444383, "err": 0.33 },
 "6": { "day": 6, "ori": 248.22999600000003, "pre": 275.1655046414833, "err": 10.86 },
 "7": { "day": 7, "ori": 277.970001, "pre": 274.0908452116006, "err": 1.4 },
 "8": { "day": 8, "ori": 242.21000700000002, "pre": 273.12002506404633, "err": 12.77 },
 "9": { "day": 9, "ori": 252.86000100000004, "pre": 272.246150223522, "err": 7.67 },
 "10": { "day": 10, "ori": 246.669998, "pre": 271.46433827126106, "err": 10.06 },
 "11": { "day": 11, "ori": 244.779999, "pre": 270.77152304827797, "err": 10.63 },
 "12": { "day": 12, "ori": 229.240005, "pre": 270.16577111674127, "err": 17.87 },
 "13": { "day": 13, "ori": 224.36999499999996, "pre": 269.6458130477715, "err": 20.2 },
 "14": { "day": 14, "ori": 246.880005, "pre": 269.2104184718396, "err": 9.05 },
 "15": { "day": 15, "ori": 245.520004, "pre": 268.8578297181895, "err": 9.51 },
 "16": { "day": 16, "ori": 258.440002, "pre": 268.5851954542616, "err": 3.93 },
 "17": { "day": 17, "ori": 247.740005, "pre": 268.3882972702414, "err": 8.34 },
 "18": { "day": 18, "ori": 254.809998, "pre": 268.2612176745839, "err": 5.28 },
 "19": { "day": 19, "ori": 254.289993, "pre": 268.19596903018686, "err": 5.47 }
}

How important is this (1-5)?

3

[tyt34]

It’s a LSTM

I just now read about this architecture. I think you right.

I right understand? training: 1 day before … 999 day before in input: open, close, high, low

in output: today and prediction value - close

[robertleeplummerjr]

Here is what you need to do: https://observablehq.com/@robertleeplummerjr/brain-js-issue-528-solution-example