Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.
When I run kats' lstm model with bo code, it freezes and does not output.
`df = pd.read_csv('uni_session_neww.csv', sep=';')
df.columns = ['time', 'value']
print(df.head())
ts=TimeSeriesData(df)
ts.plot(cols=['value'])
plt.show()
logging.basicConfig(level=logging.DEBUG)
params = LSTMParams(
hidden_size=5, # number of hidden layers
time_window=6,
num_epochs=30
)
m = LSTMModel(ts, params)
m.fit(verbose=1)
fcst = m.predict(steps=720)
print(fcst.head())
When I run kats' lstm model with bo code, it freezes and does not output. `df = pd.read_csv('uni_session_neww.csv', sep=';') df.columns = ['time', 'value']
print(df.head())
ts=TimeSeriesData(df)
ts.plot(cols=['value'])
plt.show()
logging.basicConfig(level=logging.DEBUG) params = LSTMParams( hidden_size=5, # number of hidden layers time_window=6, num_epochs=30 )
m = LSTMModel(ts, params) m.fit(verbose=1) fcst = m.predict(steps=720) print(fcst.head())
m.plot() plt.title('LSTM') plt.show()`